diff --git a/.gitignore b/.gitignore index 36ea87ed9..f63c2ba4e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,15 @@ # Python __pycache__/ + +# RMA experiment runtime/build artifacts. Curated small reports live in results/rma/. +build/ +build-*/ +*.pt +*.pth +*.onnx +*.engine +*.plan +events.out.tfevents.* dist/ .eggs/ *.egg-info/ diff --git a/README.md b/README.md index 2fbfad7e6..7d608d776 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,10 @@ Contract-driven infrastructure for robot learning across physics backends and ha

Languages: English | 简体中文

+> **Fork experiment: Go2 RMA locomotion.** See the [experiment portfolio](docs/rma/README.md) +> for teacher/student code, reproducible evaluation data, learning curves and C++ mock deployment. +> Current forward-speed tracking has not reached the target; failures and reproduction limits are documented. +

CI Project Page diff --git a/README_zh.md b/README_zh.md index 4654bd779..158c83643 100644 --- a/README_zh.md +++ b/README_zh.md @@ -6,6 +6,9 @@

语言：简体中文 | English

+> **本 fork 的 Go2 RMA 运动控制实验**：[查看项目展示与实验记录](docs/rma/README.md)。 +> 包含教师/学生代码、真实评估数据、训练曲线和 C++ mock 部署；当前前进速度跟踪尚未达标。 +

CI Project Page diff --git a/deploy_cpp/CMakeLists.txt b/deploy_cpp/CMakeLists.txt new file mode 100644 index 000000000..27273da76 --- /dev/null +++ b/deploy_cpp/CMakeLists.txt @@ -0,0 +1,43 @@ +cmake_minimum_required(VERSION 3.20) +project(unilab_rma_deploy LANGUAGES CXX) +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +option(WITH_TENSORRT "Build TensorRT 10 policy inference" OFF) +option(ENABLE_TSAN "Enable thread sanitizer on supported Unix compilers" OFF) +find_package(Threads REQUIRED) +add_executable(control_loop src/main_control_loop.cpp) +add_executable(test_double_buffer tests/test_double_buffer.cpp) +add_executable(test_control_mapping tests/test_control_mapping.cpp) +foreach(target control_loop test_double_buffer test_control_mapping) + target_include_directories(${target} PRIVATE include) + target_link_libraries(${target} PRIVATE Threads::Threads) + if(MSVC) + target_compile_options(${target} PRIVATE /W4 /EHsc) + else() + target_compile_options(${target} PRIVATE -Wall -Wextra -Wpedantic) + if(ENABLE_TSAN) + target_compile_options(${target} PRIVATE -fsanitize=thread -g) + target_link_options(${target} PRIVATE -fsanitize=thread) + endif() + endif() +endforeach() +if(WITH_TENSORRT) + find_package(CUDAToolkit REQUIRED) + find_path(TENSORRT_INCLUDE NvInfer.h HINTS ${TENSORRT_ROOT}/include REQUIRED) + find_library(TENSORRT_LIBRARY nvinfer HINTS ${TENSORRT_ROOT}/lib ${TENSORRT_ROOT}/lib64 REQUIRED) + target_compile_definitions(control_loop PRIVATE WITH_TENSORRT) + target_include_directories(control_loop PRIVATE ${TENSORRT_INCLUDE}) + target_link_libraries(control_loop PRIVATE ${TENSORRT_LIBRARY} CUDA::cudart) + add_executable(policy_worker src/policy_worker.cpp) + target_include_directories(policy_worker PRIVATE include ${TENSORRT_INCLUDE}) + target_link_libraries(policy_worker PRIVATE ${TENSORRT_LIBRARY} CUDA::cudart) + if(MSVC) + target_compile_options(policy_worker PRIVATE /W4 /EHsc) + else() + target_compile_options(policy_worker PRIVATE -Wall -Wextra -Wpedantic) + endif() +endif() +enable_testing() +add_test(NAME double_buffer_consistency COMMAND test_double_buffer) +add_test(NAME mock_control_loop COMMAND control_loop --seconds 1) +add_test(NAME policy_actuator_mapping COMMAND test_control_mapping) diff --git a/deploy_cpp/include/ControlMapping.hpp b/deploy_cpp/include/ControlMapping.hpp new file mode 100644 index 000000000..c4ea953e6 --- /dev/null +++ b/deploy_cpp/include/ControlMapping.hpp @@ -0,0 +1,21 @@ +#pragma once + +#include + +namespace rma { +// Policy/observations: FL FR RL RR. Scene actuator storage: FR FL RR RL. +// Per leg: hip, thigh, calf. Verify the scene contract before hardware use. +inline constexpr std::array policyToActuator{3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8}; + +inline std::array actuatorTargets( + const std::array& home, + const std::array& policyAction, + float scale = 0.25f) { + std::array target{}; + for (int joint = 0; joint < 12; ++joint) { + const int actuator = policyToActuator[joint]; + target[actuator] = home[actuator] + scale * policyAction[joint]; + } + return target; +} +} // namespace rma diff --git a/deploy_cpp/include/DoubleBuffer.hpp b/deploy_cpp/include/DoubleBuffer.hpp new file mode 100644 index 000000000..9d475c208 --- /dev/null +++ b/deploy_cpp/include/DoubleBuffer.hpp @@ -0,0 +1,54 @@ +#pragma once +#include +#include +#include + +// Single producer / single consumer, latest-value mailbox, exactly two payload slots. +// Publication alone is insufficient: slot ownership prevents payload data races. +// Nonblocking try operations: caller retains last valid value on read failure. +template class DoubleBuffer { + static_assert(std::is_trivially_copyable_v, "Payload must be trivially copyable"); + static_assert(std::atomic::is_always_lock_free, "Requires lock-free atomic"); + enum { Free=0, Writing=1, Ready=2, Reading=3 }; + struct alignas(64) Slot { + std::atomic state{Free}; + T value{}; + }; + std::array slots_{}; + std::atomic published_{-1}; +public: + bool tryWrite(const T& value) noexcept { + const int previous=published_.load(std::memory_order_acquire); + const int first=previous<0?0:1-previous; + for(int attempt=0;attempt<2;++attempt){ + const int index=attempt==0?first:1-first; + auto& slot=slots_[index]; + int state=slot.state.load(std::memory_order_acquire); + if(state!=Free && state!=Ready) continue; + if(!slot.state.compare_exchange_strong(state,Writing, + std::memory_order_acq_rel,std::memory_order_relaxed)) continue; + slot.value=value; + // Announce while Writing, then release the payload. A reader of an + // old index cannot consume a recycled slot before its publication; + // Ready acquire also orders the publication before its next read. + published_.store(index,std::memory_order_release); + slot.state.store(Ready,std::memory_order_release); + return true; + } + return false; + } + bool tryRead(T& value) noexcept { + const int index=published_.load(std::memory_order_acquire); + if(index<0) return false; + auto& slot=slots_[index]; + int state=Ready; + if(!slot.state.compare_exchange_strong(state,Reading, + std::memory_order_acq_rel,std::memory_order_relaxed)) return false; + value=slot.value; + slot.state.store(Free,std::memory_order_release); + return true; + } + DoubleBuffer()=default; + DoubleBuffer(const DoubleBuffer&)=delete; + DoubleBuffer& operator=(const DoubleBuffer&)=delete; +}; diff --git a/deploy_cpp/include/TensorRTPolicy.hpp b/deploy_cpp/include/TensorRTPolicy.hpp new file mode 100644 index 000000000..a6adfdf0b --- /dev/null +++ b/deploy_cpp/include/TensorRTPolicy.hpp @@ -0,0 +1,80 @@ +#pragma once +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class TensorRTPolicy { + struct Logger : nvinfer1::ILogger { + void log(Severity severity,const char* msg) noexcept override { + if(severity<=Severity::kERROR) std::fprintf(stderr,"TensorRT: %s\n",msg); + } + } logger_; + std::unique_ptr runtime_; + std::unique_ptr engine_; + std::unique_ptr context_; + cudaStream_t stream_{}; + void *obs_{},*history_{},*action_{}; + static void check(cudaError_t result){ + if(result!=cudaSuccess) throw std::runtime_error(cudaGetErrorString(result)); + } + void cleanup() noexcept { + if(stream_) cudaStreamSynchronize(stream_); + if(obs_) cudaFree(obs_); + if(history_) cudaFree(history_); + if(action_) cudaFree(action_); + if(stream_) cudaStreamDestroy(stream_); + } + void require(const char* name,nvinfer1::TensorIOMode mode,std::initializer_list shape){ + if(engine_->getTensorIOMode(name)!=mode || engine_->getTensorDataType(name)!=nvinfer1::DataType::kFLOAT || + engine_->getTensorLocation(name)!=nvinfer1::TensorLocation::kDEVICE) + throw std::runtime_error(std::string("Engine IO type/mode mismatch: ")+name); + const auto dims=engine_->getTensorShape(name); + if(dims.nbDims!=static_cast(shape.size())) throw std::runtime_error("Engine rank mismatch"); + int i=0; + for(int d:shape) if(dims.d[i++]!=d) throw std::runtime_error("Engine shape mismatch"); + } +public: + explicit TensorRTPolicy(const std::string& path){ + try { + std::ifstream file(path,std::ios::binary); + if(!file) throw std::runtime_error("Cannot open TensorRT engine"); + std::vector bytes((std::istreambuf_iterator(file)),{}); + runtime_.reset(nvinfer1::createInferRuntime(logger_)); + if(!runtime_) throw std::runtime_error("Cannot create TensorRT runtime"); + engine_.reset(runtime_->deserializeCudaEngine(bytes.data(),bytes.size())); + if(!engine_) throw std::runtime_error("Cannot deserialize engine"); + if(engine_->getNbIOTensors()!=3) throw std::runtime_error("Expected exactly 3 IO tensors"); + require("observation",nvinfer1::TensorIOMode::kINPUT,{1,49}); + require("history",nvinfer1::TensorIOMode::kINPUT,{1,50,49}); + require("action",nvinfer1::TensorIOMode::kOUTPUT,{1,12}); + context_.reset(engine_->createExecutionContext()); + if(!context_) throw std::runtime_error("Cannot create execution context"); + check(cudaStreamCreate(&stream_)); + check(cudaMalloc(&obs_,49*sizeof(float))); + check(cudaMalloc(&history_,2450*sizeof(float))); + check(cudaMalloc(&action_,12*sizeof(float))); + if(!context_->setTensorAddress("observation",obs_) || + !context_->setTensorAddress("history",history_) || + !context_->setTensorAddress("action",action_)) throw std::runtime_error("Cannot bind IO"); + } catch(...) { cleanup(); throw; } + } + ~TensorRTPolicy(){ cleanup(); } + void infer(const std::array& obs,const std::array& history, + std::array& action){ + check(cudaMemcpyAsync(obs_,obs.data(),sizeof(obs),cudaMemcpyHostToDevice,stream_)); + check(cudaMemcpyAsync(history_,history.data(),sizeof(history),cudaMemcpyHostToDevice,stream_)); + if(!context_->enqueueV3(stream_)) throw std::runtime_error("enqueueV3 failed"); + check(cudaMemcpyAsync(action.data(),action_,sizeof(action),cudaMemcpyDeviceToHost,stream_)); + check(cudaStreamSynchronize(stream_)); + } + TensorRTPolicy(const TensorRTPolicy&)=delete; + TensorRTPolicy& operator=(const TensorRTPolicy&)=delete; +}; diff --git a/deploy_cpp/src/main_control_loop.cpp b/deploy_cpp/src/main_control_loop.cpp new file mode 100644 index 000000000..194c3dea0 --- /dev/null +++ b/deploy_cpp/src/main_control_loop.cpp @@ -0,0 +1,195 @@ +// Timing/IO framework. MOCK ROBOT ONLY: no Unitree SDK commands are sent. +// C++17, TensorRT 10 optional, static FP32 IO with optional internal FP16 engine. +#include "DoubleBuffer.hpp" +#include "ControlMapping.hpp" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#ifdef WITH_TENSORRT +#include "TensorRTPolicy.hpp" +#endif + +using Clock=std::chrono::steady_clock; +std::int64_t nowNs(){return std::chrono::duration_cast(Clock::now().time_since_epoch()).count();} +struct Sensor { + // q,dq in joint order FL FR RL RR; q0 is supplied in actuator order FR FL RR RL. + std::array q{},dq{}; + std::array gyro{},gravity{0,0,-1},command{}; + std::int64_t stamp{}; +}; +struct Command { + std::array target{}; + std::int64_t stamp{}; + std::uint64_t sequence{}; +}; +struct ControlConfig { + std::array q0{},kp{},kd{},limit{}; + ControlConfig(){kp.fill(35);kd.fill(.5);limit.fill(10);} + void load(const std::string& path){ + std::ifstream f(path); + if(!f) throw std::runtime_error("Cannot read control config"); + for(int i=0;i<12;++i){ + if(!(f>>q0[i]>>kp[i]>>kd[i]>>limit[i]) || !std::isfinite(q0[i]) || + !std::isfinite(kp[i]) || !std::isfinite(kd[i]) || !std::isfinite(limit[i]) || + kp[i]<0 || kd[i]<0 || limit[i]<=0) throw std::runtime_error("Invalid control config row"); + } + std::string extra; + if(f>>extra) throw std::runtime_error("Control config must contain exactly 12 rows of q0 kp kd torque_limit"); + } +}; +// Joint -> actuator index, based on copied YAML; verify against materialized scene before real use. +constexpr auto jointToAct=rma::policyToActuator; + +int main(int argc,char** argv){ + try { + int seconds=5; + std::string enginePath, configPath; + for(int i=1;i3600) throw std::runtime_error("seconds must be 1..3600"); + ControlConfig cfg; + if(!configPath.empty()) cfg.load(configPath); + if(!enginePath.empty() && configPath.empty()) throw std::runtime_error("Engine requires matching home/PD config"); +#ifdef WITH_TENSORRT + std::unique_ptr policy; + if(!enginePath.empty()){ + policy=std::make_unique(enginePath); + // Trigger lazy CUDA/kernel initialization before timed control starts. + std::array warmObs{}; + std::array warmHistory{}; + std::array warmAction{}; + policy->infer(warmObs,warmHistory,warmAction); + for(float a:warmAction) if(!std::isfinite(a)) throw std::runtime_error("Non-finite warm-up action"); + std::cout<<"TensorRT engine loaded; IO checked; warm-up complete\n"; + } +#else + if(!enginePath.empty()) throw std::runtime_error("Rebuild with WITH_TENSORRT=ON"); +#endif + std::cout<<"MOCK ROBOT ONLY; "<<(enginePath.empty()?"zero-action mock policy":"TensorRT policy") + <<"; 50 Hz policy / 1000 Hz PD requested\n"; + DoubleBuffer sensors; + DoubleBuffer commands; + std::atomic stop{false}; + std::atomic fastTicks{0},slowTicks{0},stale{0},fastMiss{0},slowMiss{0}; + // Thread-owned statistics; main reads them only after both joins. + std::uint64_t commandsPublished=0,commandsConsumed=0; + float maxActionAbs=0.f,maxTargetOffset=0.f,maxTorqueAbs=0.f; +#ifdef WITH_TENSORRT + std::uint64_t inferenceCalls=0; + double inferenceTotalMs=0.,inferenceMaxMs=0.; +#endif + std::string failure; // Written only by slow thread; read after join. + const auto end=Clock::now()+std::chrono::seconds(seconds); + std::thread fast([&]{ + Sensor s; + for(int j=0;j<12;++j) s.q[j]=cfg.q0[jointToAct[j]]; + Command active; + auto deadline=Clock::now(); + while(!stop.load(std::memory_order_acquire) && Clock::now()active.sequence){ + active=candidate; + ++commandsConsumed; + } + const bool fresh=active.sequence>0 && s.stamp>=active.stamp && s.stamp-active.stamp<100000000; + if(!fresh) ++stale; + for(int j=0;j<12;++j){ + const int a=jointToAct[j]; + float torque=fresh?cfg.kp[a]*(active.target[a]-s.q[j])-cfg.kd[a]*s.dq[j]:0.f; + torque=std::clamp(torque,-cfg.limit[a],cfg.limit[a]); + maxTorqueAbs=std::max(maxTorqueAbs,std::abs(torque)); + // Mock plant, no gravity/contact. Replace with timestamped SDK sensor/command IO. + s.dq[j]+=(torque-.2f*s.dq[j])/.2f*.001f; + s.q[j]+=s.dq[j]*.001f; + } + ++fastTicks; + deadline+=std::chrono::milliseconds(1); + if(Clock::now()>deadline){++fastMiss;deadline=Clock::now();} + std::this_thread::sleep_until(deadline); + } + stop.store(true,std::memory_order_release); + }); + std::thread slow([&]{ + try { + std::array history{}; + std::array previous{}; + auto deadline=Clock::now(); + std::uint64_t sequence=0; + float phase=0.f; + while(!stop.load(std::memory_order_acquire)){ + Sensor s; + if(sensors.tryRead(s) && nowNs()-s.stamp<20000000){ + std::array obs{}; + for(int i=0;i<3;++i){obs[i]=s.gyro[i];obs[3+i]=s.gravity[i];obs[42+i]=s.command[i];} + for(int j=0;j<12;++j){obs[6+j]=s.q[j]-cfg.q0[jointToAct[j]];obs[18+j]=s.dq[j];obs[30+j]=previous[j];} + // Reset phase on process start. Real runtime must match environment phase resets. + constexpr float offsets[4]={0,.5f,.5f,0}; + for(int i=0;i<4;++i) obs[45+i]=std::fmod(phase+offsets[i],1.f); + // Shift left explicitly; no overlapping std::copy ranges. + for(int i=0;i<2450-49;++i) history[i]=history[i+49]; + std::copy(obs.begin(),obs.end(),history.end()-49); + std::array action{}; +#ifdef WITH_TENSORRT + if(policy){ + const auto started=Clock::now(); + policy->infer(obs,history,action); + const double elapsed=std::chrono::duration(Clock::now()-started).count(); + ++inferenceCalls; + inferenceTotalMs+=elapsed; + inferenceMaxMs=std::max(inferenceMaxMs,elapsed); + } +#endif + Command cmd; + cmd.stamp=s.stamp; // Sensor age includes inference latency. + cmd.sequence=++sequence; + for(int i=0;i<12;++i){ + if(!std::isfinite(action[i])) throw std::runtime_error("Non-finite model action"); + action[i]=std::clamp(action[i],-1.f,1.f); + maxActionAbs=std::max(maxActionAbs,std::abs(action[i])); + } + cmd.target=rma::actuatorTargets(cfg.q0,action); + for(int i=0;i<12;++i){ + maxTargetOffset=std::max(maxTargetOffset,std::abs(cmd.target[i]-cfg.q0[i])); + } + if(commands.tryWrite(cmd)){ + previous=action; + ++commandsPublished; + } + phase=std::fmod(phase+.04f,1.f); // Same float32 0.02s * 2Hz update as training. + ++slowTicks; + } + deadline+=std::chrono::milliseconds(20); + if(Clock::now()>deadline){++slowMiss;deadline=Clock::now();} + std::this_thread::sleep_until(deadline); + } + } catch(const std::exception& e){failure=e.what();stop.store(true,std::memory_order_release);} + }); + fast.join();slow.join(); + std::cout<<"PD ticks="<0) std::cout<<"TensorRT calls="< +#include +#include +#include + +int main(int argc,char** argv){ + static_assert(sizeof(float)==4 && std::numeric_limits::is_iec559, + "The worker protocol requires IEEE754 float32"); + try { + if(argc!=3 || std::string(argv[1])!="--engine") + throw std::runtime_error("Usage: policy_worker --engine file.engine"); + const std::uint32_t endian=1; + if(*reinterpret_cast(&endian)!=1) + throw std::runtime_error("The worker protocol requires little endian"); + TensorRTPolicy policy(argv[2]); + std::array obs{}; + std::array history{}; + std::array action{}; + policy.infer(obs,history,action); // Finish CUDA initialization before handshake. + for(float value:action) if(!std::isfinite(value)) + throw std::runtime_error("Non-finite warm-up output"); + const std::array dimensions{49,50,12}; + std::cout.write("RMA1",4); + std::cout.write(reinterpret_cast(dimensions.data()),sizeof(dimensions)); + std::cout.flush(); + while(true){ + std::cin.read(reinterpret_cast(obs.data()),sizeof(obs)); + if(std::cin.gcount()==0 && std::cin.eof()) break; + if(!std::cin) throw std::runtime_error("Incomplete observation frame"); + std::cin.read(reinterpret_cast(history.data()),sizeof(history)); + if(!std::cin) throw std::runtime_error("Incomplete history frame"); + for(float value:obs) if(!std::isfinite(value)) + throw std::runtime_error("Non-finite observation input"); + for(float value:history) if(!std::isfinite(value)) + throw std::runtime_error("Non-finite history input"); + policy.infer(obs,history,action); + for(float value:action) if(!std::isfinite(value)) + throw std::runtime_error("Non-finite action output"); + std::cout.write(reinterpret_cast(action.data()),sizeof(action)); + std::cout.flush(); + if(!std::cout) throw std::runtime_error("Cannot write action frame"); + } + return 0; + } catch(const std::exception& error){ + std::cerr<<"TensorRT worker: "< +#include +#include + +int main() { + // Independent expected destinations; nonuniform home values expose swaps. + constexpr std::array expected{3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8}; + std::array home{}; + for (int index = 0; index < 12; ++index) home[index] = 0.1f * index; + for (int joint = 0; joint < 12; ++joint) { + std::array action{}; + action[joint] = 1.0f; + const auto target = rma::actuatorTargets(home, action); + for (int actuator = 0; actuator < 12; ++actuator) { + const float wanted = home[actuator] + (actuator == expected[joint] ? 0.25f : 0.0f); + if (std::abs(target[actuator] - wanted) > 1e-6f) { + std::cerr << "Wrong target for policy joint " << joint + << ", actuator " << actuator << '\n'; + return 1; + } + } + } + std::cout << "All 12 one-hot policy actions reach the expected actuator\n"; + return 0; +} diff --git a/deploy_cpp/tests/test_double_buffer.cpp b/deploy_cpp/tests/test_double_buffer.cpp new file mode 100644 index 000000000..6d03ac6c9 --- /dev/null +++ b/deploy_cpp/tests/test_double_buffer.cpp @@ -0,0 +1,39 @@ +#include "DoubleBuffer.hpp" +#include +#include +#include +#include +#include +struct Payload {std::uint64_t sequence{};std::array values{};}; +int main(){ + DoubleBuffer buffer; + Payload first; + if(buffer.tryRead(first)) return 1; + std::atomic done{false}; + std::atomic errors{0}; + std::uint64_t count=0; + std::thread writer([&]{ + for(std::uint64_t i=1;i<=200000;++i){ + Payload p;p.sequence=i;p.values.fill(i); + while(!buffer.tryWrite(p)) std::this_thread::yield(); + } + done.store(true,std::memory_order_release); + }); + std::thread reader([&]{ + std::uint64_t last=0; + auto verify=[&](const Payload& p){ + if(p.sequence<=last) ++errors; + for(auto v:p.values) if(v!=p.sequence) ++errors; + last=p.sequence;++count; + }; + while(!done.load(std::memory_order_acquire)){ + Payload p; + if(buffer.tryRead(p)) verify(p); + } + Payload p; + if(buffer.tryRead(p)) verify(p); + }); + writer.join();reader.join(); + std::cout<<"snapshots="<0?0:1; +} diff --git a/docs/rma/EXPERIMENT_WORKFLOW.md b/docs/rma/EXPERIMENT_WORKFLOW.md new file mode 100644 index 000000000..25a919034 --- /dev/null +++ b/docs/rma/EXPERIMENT_WORKFLOW.md @@ -0,0 +1,151 @@ +# 从一次评估到一份可复查的实验记录 + +当前步骤与完整命令导航见 [项目工作流](PROJECT_WORKFLOW.md)。 + +## 先分清三种“完成” + +组件验证：RMA 接口、历史缓冲、导出接口、C++ mock 等已有测试。 +框架兼容：本地运行副本是 1.3.3，GitHub fork 基线是 1.3.4，整仓类型检查尚未通过。 +任务效果：已有教师未达到 0.5 m/s 前进指令。配置能运行，不意味着策略已经学会任务。 + +后续完整接入应先固定一套源码版本，在独立环境里匹配依赖和资产，完成整仓门禁， +再做真实环境的训练、评估、ONNX/TensorRT 数值与任务效果验证。 +不要把新 fork 整个覆盖到现有可运行目录，也不要在现有环境盲目升级所有依赖。 +目前仍以原可运行目录的教师作为诊断对象；本次仅增量更新评估追踪与部署映射代码。 + +## 第一件事：给现有模型记录数据 + +这是评估，不更新网络权重。训练入口和奖励配置不需要为记录数据而修改。 +`--trace-dir` 是可选开关；不开启时不导入追踪器、不创建文件、不读取逐环境奖励。 +开启后仅评估阶段多出文件写入和 CPU 拷贝，不能承诺记录本身零开销。 + +在已经匹配好依赖和资产的原运行目录中执行： + +```bash +cd /home/abc/my_unilab +uv run --no-sync python scripts/evaluate_rma.py \ + --policy teacher \ + --checkpoint runs/rma/teacher_forward_v1.pt \ + --task go2_joystick_flat/rma_forward \ + --num-envs 8 --steps 1000 --seed 123 \ + --command 0.5 0 0 --diagnostics \ + --trace-dir runs/rma/evaluations/EXP-002/seed123 \ + --trace-warmup-steps 100 +``` + +每次都会在指定父目录下创建独立的 `eval_` 子目录，重复运行不覆盖旧记录。 +终端 JSON 的 `trace.directory` 给出本次的准确绝对路径。 + +| 文件 | 保存什么 | +|---|---| +| `transitions.csv` | 每一步、每个环境一行：目标/实际速度、12 维动作、关节相对角度/角速度、奖励分项、回合编号及终止标记 | +| `run_config.json` | 命令参数、checkpoint SHA256、可获取的 Git 信息、关键源码 SHA256、实际控制周期、关节顺序和 scale/offset | +| `task_source_snapshot/*.yaml` | 当次运行目录的任务源配置，包含可用的消融任务；不是完整物化 Hydra 配置，也不包含资产或依赖 | +| `summary.json` | 原评估结果，以及动作范围、动作接近边界比例、全程和启动窗口外的速度误差 | + +1000 步 × 8 环境应得到 8000 行数据，另加一行 CSV 表头。 +`before_*` 对应动作执行前的状态，`after_*` 对应执行后的终止状态；结束回合时不会误记重置后的姿态。 +`episode_id` 与 `episode_step` 将各环境的不同回合分开。 + +`reward_rate/` 是**已经乘奖励权重、还没有乘控制 dt** 的分项。 +`reward` 才是该环境这一步实际返回的总奖励。不要直接把分项 rate 与 reward 比大小。 +当前任务使用 dt 缩放时，分项 rate 之和乘 dt 才能与该步 reward 核对；不能跨不同权重直接比较总奖励。 + +`normalized_action_near_bound_fraction` 统计 `|action| >= 0.95` 的比例。 +它只是 tanh 动作接近输出边界的指标，不能证明电机扭矩饱和。 + +启动窗口外的指标逐回合排除前 100 步；0.02 s 控制周期时对应前 2 s。 +这只是约定的分析窗口，不保证系统已经进入稳态；全程指标同时保留。 +不足该窗口的回合没有有效样本时，指标为 `null`，不伪造为零误差。 + +## 第二件事：画图，再对照看数据 + +将下面的路径换成上一条命令输出的实际 `trace.directory`： + +```bash +uv run --no-sync python scripts/plot_evaluation_trace.py \ + /home/abc/my_unilab/runs/rma/evaluations/EXP-002/seed123/eval_ +``` + +生成 `plots/velocity_tracking.png`、`action_distribution.png`、`reward_components.png`。 +速度图显示各环境均值及跨环境标准差；包含重置，不把标准差称作置信区间。 +CSV 保留各个环境和回合，可进一步检查平均值是否掩盖个别异常。 + +本次已经生成的第一份真实记录位于 +`/home/abc/my_unilab/runs/rma/evaluations/EXP-002/seed123/eval_hhlzyanz/`。 +仓库中另发布了 [压缩数据与图](../../results/rma/diagnostics/EXP-002/seed123/)； +无需仿真环境即可对发布副本运行下面的命令重新画图： + +```bash +uv run --no-sync python scripts/plot_evaluation_trace.py \ + results/rma/diagnostics/EXP-002/seed123 +``` + +| 先看什么 | 怎么判断 | 还不能推出什么 | +|---|---|---| +| 指令列 | `command_vx_m_s` 是否一直为 0.5，vy/wz 是否为 0 | 期望值正确不代表动力学/控制正确 | +| 速度列与误差 | `before_vx_m_s` 是否接近 0.5，侧向和转向是否偏离 | 只看总奖励不能代替速度指标 | +| 动作分布 | 是否几乎不变化、偏置很大、频繁接近 ±1 | 任一现象都不是根因的充分证明 | +| 奖励分项 | 哪些项贡献大，跟踪项是否与速度一致 | 分项大不自动说明权重必须减小 | +| 控制映射 | `action_target_joint_order` 与 `joint_observation_order` 是否正确对应 | 两种顺序不同不一定是错误，框架可能已正确映射 | + +当前源配置的观测关节和运行时动作目标均按 FL/FR/RL/RR，场景执行器存储按 FR/FL/RR/RL。 +追踪文件保存**运行时解析出来的**关节名、索引、scale 与 offset，而不是拿观测顺序猜动作顺序。 +在没有 clip/encoder bias 修正的简化情形，关节目标是 `offset + scale * action`，当前配置 scale 为 0.25。 +这里记录的是配置映射和观测，不是实际驱动器写入目标/扭矩；进一步确认执行效果需要专门记录那些信号。 +不能因为腿编号顺序不一样就直接交换策略输出。 + +本次追踪发现旧 ONNX 元数据将动作顺序误写成执行器存储顺序，旧 C++ mock 也直接按相同索引相加。 +现已修正输出边界的 12 维映射并新增非零 one-hot 测试；`last_action` 仍保持策略原顺序。 +旧 JSON 中的 `action_actuator_order` 不能再作为物理存储顺序使用；重新导出后同时记录 +`action_joint_order`、`scene_actuator_order` 和 `action_to_actuator_indices`。 +此错误影响独立 C++ 路径，不是 Python/UniLab 教师原地踏步的已确认原因。 + +## 第三件事：只选一个变量做新实验 + +先用同一模型完成种子 123、456、789 的诊断，再选择最有证据支持的假设。 +例如怀疑摆腿辅助奖励干扰前进：复制当前前进任务为新的实验配置，只改 +`reward.swing_feet_z.weight` 一项，记录旧值和新值。 +新数值应在实验前选定并记录，不能在多个候选中挑最好结果后只公开那一次。 + +这是下一次重新训练时的配置变化；仅给旧 checkpoint 换奖励做评估不会让它学会新动作。 +训练初始化、指令范围、PPO 参数、控制器、训练预算和评估协议保持一致。 +新模型使用新文件名，保留旧模型；同样协议下比较实际速度与 RMSE。 +不要同一轮又加训练量、又改奖励、又换控制器，那样无法判断是谁产生作用。 + +首个独立消融现已准备为 [EXP-003](../../experiments/rma/EXP-003_no_pose_penalty.md)： +仅将 `similar_to_default.weight` 从 -0.1 设为 0.0，从头训练并使用新输出文件。 +完整命令、日志位置和评估协议见该记录。现已完成训练及三个种子评估，未观察到实质前进改善。 +随后控制诊断发现当前 1.3.3 运行副本的快速目标写入有顺序错位；先处理控制路径再开始下一次训练。 + +## GitHub：数据、Issue、PR 各自放什么 + +| 位置 | 职责 | 更新频率 | +|---|---|---| +| 本地 `runs/rma/...` | 自动保存完整 CSV、checkpoint、运行配置 | 每次运行自动产生；不把所有大文件上传 Git | +| `experiments/rma/EXP-xxx.md` | 假设、唯一变化、命令、数据位置、结果、结论 | 每个有明确问题的实验一份，使用 TEMPLATE.md | +| `results/rma/` | 精选小数据、摘要和曲线，支持复查 | 整理有价值的结果后提交 | +| Issue #1 | “原地踏步”这个问题的调查过程，链接数据/实验/提交 | 有新证据或新结论时追加评论 | +| PR #2 | 当前实验分支相对 main 的代码变化、测试和审查 | 提交并 push 后代码差异自动更新；变更范围/验证结论变化时更新说明 | + +在 Issue #1 的 Add a comment 中写进展，再点 Comment 即可。 +不需要每次实验都在 Issue 和 PR 粘贴两份相同日志。 +同一问题的后续证据继续放 Issue #1；独立新问题才单独开 Issue。 +PR 是代码变更的审查入口，不是每次实验都新建或反复粘贴日志的地方。 +现在 PR #2 的内容在实验分支，尚未合入 main，查看文件时要选对分支。 + +一条适合 Issue 的评论模板： + +```text +实验：EXP-002，检查原地踏步 +假设：待诊断；本次不改算法和奖励 +模型：teacher_forward_v1.pt，SHA256 见 run_config.json +协议：0.5/0/0，8 环境 × 1000 步，种子 123/456/789 +唯一变化：仅开启评估追踪 +数据/配置/代码：填写已提交的链接；本地绝对路径只是自己定位用 +结果：填写每个种子的平均 vx、vx RMSE、动作边界比例及关键分项 +结论：证据支持什么，仍不能确定什么 +下一步：只测试哪个变量，以及为什么 +``` + +“期望它学会走路”是目标；“测量到多少、改了什么、能排除什么”才是实验记录。 diff --git a/docs/rma/PROJECT_WORKFLOW.md b/docs/rma/PROJECT_WORKFLOW.md new file mode 100644 index 000000000..1ac17268b --- /dev/null +++ b/docs/rma/PROJECT_WORKFLOW.md @@ -0,0 +1,158 @@ +# 项目导航：现在做什么，完成后做什么 + +更新日期：2026-10-23。所有 shell 命令在 WSL Ubuntu 执行，工作目录为 +`/home/abc/my_unilab`，使用该目录现有 uv 环境；不要覆盖 `.venv` 或全部同步 fork。 + +## 当前停在哪一步 + +EXP-003 已训练 3000 次更新，并完成三个评估种子。平均前向速度约 0.013 m/s， +没有完成 0.5 m/s 的指令跟踪。三组评估数据和三类图均已产生。 +**目标写入现已修复并验证，下一步从头训练修复后的 std=0.5 基线。** +随后再比较只收紧 std 的实验组；完整操作与修改教程见 +[EXP-004 控制修复](../../experiments/rma/EXP-004_control_mapping.md) 与 +[EXP-005 跟踪宽度](../../experiments/rma/EXP-005_tracking_width.md)。 +不要重新执行已有正式训练来生成图片，也不要同时改动作缩放、奖励和训练预算。 + +短诊断（1 环境 × 500 步）实测目录： +`/home/abc/my_unilab/runs/rma/control_diagnostics/EXP-003/eval_2ffou4yp`。 +目标映射残差最大约 0.47117 rad；前左/前右大腿目标与实际角度 RMSE 约 0.364/0.305 rad。 +修复前，1.3.3 快速路径误差 0.0300000 rad、控制诊断残差 0.47117 rad。 +修复后，12 个非零关节目标 × 2 环境（含部分重置）的映射检查最大误差 0； +500 步策略诊断映射残差 0。修复代码已备份，未触碰旧模型。 +单变量配置对照已解析，且两组各 1 次更新的入口冒烟通过； +正式修复后基线与 std 实验尚未训练。细节及修改教学见 EXP-004/EXP-005。 + +## 现在可执行的命令 + +### 1. 看已有三类图：不运行仿真，不训练 + +以下循环已执行，重复执行只重新生成这些图片。每个种子三张，共九张： + +```bash +cd /home/abc/my_unilab +for trace in runs/rma/evaluations/EXP-003/20261023_151848/seed*/eval_*; do + uv run --no-sync python scripts/plot_evaluation_trace.py "$trace" || break +done +cd /home/abc/my_unilab/runs/rma/evaluations/EXP-003/20261023_151848/seed123/eval_kwg2tw0_/plots +explorer.exe . +``` + +查看完后执行 `cd /home/abc/my_unilab` 回到项目根目录。 + +| 图 | 要回答的问题 | 当前不能推出的结论 | +|---|---|---| +| velocity_tracking.png | 实际 vx 是否接近 0.5，vy/wz 是否偏离？ | 速度小不直接证明训练预算不足 | +| action_distribution.png | 12 个动作是否变化、偏置、频繁到边界？ | 动作到边界不等于扭矩饱和 | +| reward_components.png | 各加权奖励分项是否与任务行为一致？ | 改奖励后总 reward 更高不一定走得更好 | + +### 2. 看控制图：已运行，可按需复现 + +```bash +cd /home/abc/my_unilab +uv run --no-sync python scripts/diagnose_rma_control.py \ + --checkpoint runs/rma/teacher_forward_no_pose_v1.pt \ + --task go2_joystick_flat/rma_forward_no_pose \ + --steps 500 --seed 123 --command 0.5 0 0 \ + --output-dir runs/rma/control_diagnostics/EXP-003 +``` + +每次生成新的 `eval_`，不改权重。终端给出准确目录。 +看 `control_tracking.png`：蓝线为实际写入的目标，橙线为一步后的关节角度； +`control_signals.csv` 保存逐关节信号，`control_summary.json` 保存误差。 +`mapping_residual_rad` 比较按语义关节重排后的实际缓冲与 processed target 减 encoder bias， +应接近浮点数值误差。先消除映射残差，再解释姿态跟踪误差。 +`pd_demand_estimate_nm` 仅为控制边界处的代数估计，未包含物理子步或被动阻尼， +当前公开 backend API 没有实测执行器扭矩，不能据此宣称扭矩饱和。 + +### 3. 下一项工程任务：控制路径修复与回归 + +更新：修复和验收已完成，备份、源码差异与运行命令见 EXP-004。 +需要复查时执行 `uv run --no-sync python scripts/check_rma_mapping.py`。 +这会写入测试用控制缓冲但不推进物理仿真，并在每个检查后恢复缓冲。 + +## 完整工作流：每次只进入当前满足条件的一行 + +| 阶段 | 做什么 | 完成标准与下一步 | +|---|---|---| +| 0 环境与接口 | 冒烟训练、形状与 reset 测试；确认运行源码版本、资产、dt、动作顺序 | 能运行且控制语义正确，才进入正式训练 | +| 1 教师基线 | 固定任务、种子和预算，从头 PPO，保存独立输出 | 得到模型和训练日志；进入定量评估 | +| 2 固定评估 | 同一模型、0.5/0/0、三个种子、8 环境 × 1000 步，保存 trace | 每种子有 summary/CSV/config；进入离线绘图 | +| 3 看图与诊断 | 速度、动作、奖励三图；必要时目标与实际角度图 | 本轮映射修复已验证；现在进入修复后的新基线训练 | +| 4 单变量实验 | 写假设、只改一个变量；新文件名、从头训练、同协议评估 | 记录改善或无改善；控制修复应独立编号，不能混入奖励消融 | +| 5 好教师 → 学生 | 冻结教师与 Actor，用历史估计隐变量 | 学生任务指标接近合格教师，检查 latent/action 误差；低 MSE 单独不足 | +| 6 ONNX | Adaptation + Actor 导出，验证数值误差 | 图结构、IO、顺序、历史重置和数值检查通过 | +| 7 TensorRT/C++ 闭环 | 先对齐推理输出，再用相同观测与关节映射控制仿真 | 数值、任务表现、延迟和过期指令处理逐项验收；mock 通过不等于闭环通过 | +| 8 跨引擎 Sim2Sim | 保持 IO/控制合同，换物理后端独立评估 | 报告差异和泛化结果；之后另行准备真机 | + +### 阶段 1/4：未来新实验训练模板（现在不要执行长训） + +映射修复验收后，先用相同基础任务建立新的控制修复基线；下面设置保持已有预算， +不是速度或温度保证。模型名 `teacher_mapping_v1.pt` 必须尚不存在。 + +```bash +cd /home/abc/my_unilab +uv run --no-sync python scripts/train_rma.py \ + --phase teacher --task go2_joystick_flat/rma_forward \ + --output runs/rma/teacher_mapping_v1.pt \ + --num-envs 64 --iterations 3000 --device cuda +``` + +不传 `--checkpoint` 表示新初始化。传入只加载权重，不恢复 optimizer/RNG，不能称精确续训。 +新输出不能覆盖旧模型。训练日志自动在 `teacher_mapping_v1_logs//`： +`metrics.csv`、`run_config.json`、`tensorboard/`。训练不开可视化。 +另一个终端可开 TensorBoard，若已有服务直接访问它，换端口只用于确实需要的新服务： + +```bash +cd /home/abc/my_unilab +uv run --no-sync python -m tensorboard.main \ + --logdir /home/abc/my_unilab/runs/rma --host 127.0.0.1 --port 6007 +``` + +浏览器打开 `http://127.0.0.1:6007`。训练 reward 曲线用于看优化过程； +实验结论以固定评估的实际速度/误差等任务指标为准。 + +### 阶段 2：训练完成后的标准评估模板 + +模型和任务要对应；用同一任务比较不同模型时明确区分训练环境与评估环境。 + +```bash +cd /home/abc/my_unilab +for seed in 123 456 789; do + uv run --no-sync python scripts/evaluate_rma.py \ + --policy teacher --checkpoint runs/rma/teacher_mapping_v1.pt \ + --task go2_joystick_flat/rma_forward \ + --num-envs 8 --steps 1000 --seed "$seed" \ + --command 0.5 0 0 --diagnostics \ + --trace-dir "runs/rma/evaluations/mapping_v1/seed${seed}" || break +done +``` + +之后对终端输出的每个 trace.directory 执行画图脚本。要肉眼看已有模型时单独执行： + +```bash +uv run --no-sync python scripts/evaluate_rma.py \ + --policy teacher --checkpoint runs/rma/teacher_forward_no_pose_v1.pt \ + --task go2_joystick_flat/rma_forward_no_pose \ + --num-envs 1 --steps 500 --seed 123 --command 0.5 0 0 \ + --render --render-speed 0.5 +``` + +这只是播放当前失败模型，不能修复它。 + +### 阶段 5—8：先满足前置条件 + +学生/导出命令和 TensorRT/C++ 说明见 [复现指南](REPRODUCING.md)、 +[RMA 主页](README.md) 及 `deploy_cpp/` 中的实现。当前已有冒烟链路记录， +无需反复导出失败策略。先获得合格教师，再以新学生输出名训练并固定评估。 +选择迭代数必须依据学生任务指标，不以一次 latent MSE 下降决定完成。 + +## 每次实验结束后写四句话 + +在 `experiments/rma/EXP-xxx.md` 中写：问题是什么；唯一改变是什么； +固定协议下测到什么；证据支持什么、下一步只检查什么。 +本地 runs 保存完整大数据；results 保存可复查的小摘要与精选图； +Issue #1 记录问题的新证据并链接实验；PR #2 审查代码和验证结果， +不必在两处粘贴相同日志。 + +当前核心检查：47 项 RMA 测试通过；整仓门禁仍有既有类型检查问题。 +测试通过不代表机器人已经会走路；该分支保持 Draft。 diff --git a/docs/rma/README.md b/docs/rma/README.md new file mode 100644 index 000000000..18d1926b3 --- /dev/null +++ b/docs/rma/README.md @@ -0,0 +1,120 @@ +# Go2 RMA Locomotion Lab + +基于 UniLab 的 Go2 强化学习运动控制实验：教师 PPO、历史观测适应、模型导出与 C++ 模拟部署。 + +> 当前状态：工程组件已有测试与运行记录；教师尚未学会稳定跟踪 0.5 m/s 前进指令。 +> 本仓库保留失败结果和可复现的诊断，不声称完成鲁棒行走、真机部署或跨物理引擎泛化。 + +## 这个项目解决什么问题 + +目标是研究如何让四足机器人按速度指令运动，以及如何通过历史观测估计摩擦、负载相关的隐变量。 +当前第一项任务是定位“策略原地踏步但没有向前移动”的原因。 + +**不知道下一步做什么时，先看 [项目工作流与当前步骤](PROJECT_WORKFLOW.md)。** +当前运行副本的目标映射已修复；先建立修复后基线，再执行 +[std 单变量实验与修改教程](../../experiments/rma/EXP-005_tracking_width.md)。 + +```mermaid +flowchart LR + P["训练特权参数：摩擦、负载"] --> E["教师 Encoder"] + E --> Z["隐变量 z：8 维"] + H["过去 50 步观测：50 × 49"] --> A["学生 Adaptation MLP"] + A --> ZH["估计隐变量 z_hat：8 维"] + O["当前观测：49 维"] --> ACT["共享 Actor"] + Z --> ACT + ZH --> ACT + ACT --> U["12 维动作"] +``` + +教师评估使用真实特权参数，学生使用历史估计隐变量；两条路径是分别选择的。 +部署图只保留 Adaptation + Actor，教师 Encoder 与 Critic 不进入部署图。 + +本分支在 UniLab fork 中加入增量实验层，保留 `unilab` 导入，不修改上游项目的归属。 +训练脚本使用项目内的独立 PPO 实现，不是官方 RSL-RL 的训练入口。 + +## 已观察到的结果 + +所有评估固定机体系指令 `(0.5, 0, 0)`，每个种子使用 8 个环境、每环境 1000 个控制步。 + +| 模型 | 评估种子 | 平均前向速度 (m/s) | 前向 RMSE (m/s) | 提前终止次数 | +|---|---:|---:|---:|---:| +| 原始教师 | 123 | -0.006058 | 0.510957 | 0 | +| 原始教师 | 456 | -0.011667 | 0.517436 | 1 | +| 原始教师 | 789 | -0.004922 | 0.511072 | 1 | +| 前进配置教师 v1 | 123 | 0.015686 | 0.489772 | 2 | +| 前进配置教师 v1 | 456 | 0.013730 | 0.490668 | 0 | +| 前进配置教师 v1 | 789 | 0.012254 | 0.491959 | 0 | + +前进配置 v1 已完成 3000 次更新、4,608,000 次环境转移，初始化种子为 42。 +三个评估种子不是三个独立训练种子，表格不构成跨训练种子的显著性结论。 +不同配置使用不同奖励权重，因此不能直接用总奖励比较任务效果。 +提前终止次数不是经独立检测标注的跌倒率。 + +![固定前进指令评估](../../results/rma/figures/velocity_comparison.png) + +![前进配置教师训练曲线](../../results/rma/figures/forward_v1_training.png) + +原始数值、训练记录及分析见 [results](../../results/rma/) 和 [实验记录](../../experiments/rma/)。 +曲线展示奖励而非实际速度；当前训练器尚未逐步记录前向速度曲线。 + +## 代码结构 + +```text +rsl_rl/unilab_rma/ 教师/学生网络、PPO、GAE、历史缓冲与 UniLab 适配 +scripts/ 训练、评估、导出与结果汇总 +legged_gym/ Go2 接口常量，并非重新实现 legged_gym +src/unilab/... RMA 事件与任务配置；原有 UniLab 框架保持上游归属 +deploy_cpp/ TensorRT 接口、双缓冲与 50/1000Hz mock 控制框架 +tests/rma/ 算法与接口测试 +experiments/ 问题、假设、对照设计与结论 +results/ 精选 JSON、CSV、配置记录与图表 +docs/ 使用说明、接口契约与复现边界 +``` + +## 复现与依赖 + +完整说明见 [复现指南](REPRODUCING.md) 和 [验证记录](VALIDATION.md)。 +逐步记录、画图与 Issue/PR 的分工见 [实验操作指南](EXPERIMENT_WORKFLOW.md)。 + +已发布的实测数据基于本地 UniLab 1.3.3 工作副本、MuJoCo CPU 物理环境和 PyTorch。 +本 fork 基线为 UniLab 1.3.4，基线 commit 与关键文件差异见 [provenance.json](../../results/rma/provenance.json)。 +原始运行副本未携带 Git 历史，且部分配置/运行时文件与 fork 不同；因此不能把旧实测冒充为当前分支端到端验证。 +不建议为了运行本仓库直接升级已经验证过的仿真环境。 + +核心测试与结果图表可以独立运行；真实 Go2 训练仍需要完整 UniLab、依赖与机器人资产。 +在匹配的环境中执行： + +```bash +cd /path/to/this/UniLab +uv run --no-sync python -m unittest discover -s tests/rma -p 'test_*.py' +``` + +评估已存在的前进配置教师： + +```bash +uv run --no-sync python scripts/evaluate_rma.py \ + --policy teacher --checkpoint runs/rma/teacher_forward_v1.pt \ + --task go2_joystick_flat/rma_forward \ + --num-envs 8 --steps 1000 --seed 123 \ + --command 0.5 0 0 --diagnostics +``` + +在上述命令追加 `--render` 可看画面，追加 `--render-speed 0.5` 可慢放。 +训练默认不打开可视化。实验模型权重和已安装运行环境不包含在本仓库中。 +以上是已有完整 1.3.3 运行环境的评估入口；1.3.4 环境接入必须单独完成兼容性验证。 + +## 接下来如何改进 + +1. 保留当前基线，补齐逐步速度、动作饱和和 PPO 更新诊断。 +2. 在一致训练预算下分别检验指令范围、摆腿奖励和速度跟踪权重。 +3. 教师速度跟踪通过后，比较学生自身 rollout 与教师表现。 +4. 将 ONNX / TensorRT 的数值一致性与任务表现分别验收。 + +本仓库当前不是自动课程调度器；现有 `rma_forward` 是静态前进任务配置。 +详细设计见 [下一项实验](../../experiments/rma/EXP-002_diagnosis_plan.md)。 + +## 来源与贡献范围 + +UniLab 是上游仿真与任务框架；本项目贡献范围为 RMA 增量实现、实验配置、诊断、导出/模拟部署及实验分析。 +上游来源、许可证与本地源码快照的限制见 [来源说明](THIRD_PARTY_NOTICES.md)。 +AI 工具参与了代码和文档辅助；已验证范围和未验证项以测试记录及实验数据为准。 diff --git a/docs/rma/REPRODUCING.md b/docs/rma/REPRODUCING.md new file mode 100644 index 000000000..756b01564 --- /dev/null +++ b/docs/rma/REPRODUCING.md @@ -0,0 +1,63 @@ +# RMA 实验复现与验证范围 + +## 先区分两个代码版本 + +旧实测来自没有 Git 历史的本地 UniLab 1.3.3 工作副本。 +本分支从用户 fork 的 `c19b8810059e3c495dc5394a5652d3c82c00f70f`（1.3.4）开始，导入 RMA 增量代码。 +`results/rma/provenance.json` 保存了运行依赖、来源文件哈希和关键框架文件差异。 +存在差异时，不应把旧模型成绩标成当前 Git commit 上重跑的结果。 + +## 复现表格和图表 + +在有 PyTorch 和 matplotlib 的 Python 环境中，从项目根目录执行： + +```bash +uv run --no-sync python scripts/report_rma.py +``` + +输入是 `results/rma/baseline/seed*.json`、`results/rma/forward_v1/seed*.json` +和 `results/rma/forward_v1/training_metrics.csv`。 +输出是 `results/rma/evaluation_summary.csv` 与 `results/rma/figures/*.png`。 +训练图使用真实的 3000 行 CSV，平滑线为包含启动区间的尾随 100 次更新均值。 +没有填补不存在的模型速度曲线，也没有生成虚构视频。 + +## 核心算法/接口测试与 C++ mock + +```bash +uv run --no-sync python -m unittest discover -s tests/rma -p 'test_*.py' +cmake -S deploy_cpp -B build/rma -DCMAKE_BUILD_TYPE=Release +cmake --build build/rma -j2 +ctest --test-dir build/rma --output-on-failure +``` + +这些测试验证算法接口、历史缓冲、概率/终止处理、导出接口和并发 mock。 +它们不能证明 Go2 速度跟踪、真机安全、硬实时或 TensorRT 在目标设备上的精度与延迟。 + +## 真实评估协议 + +保留原 checkpoint，固定 `(0.5, 0, 0)`，每个种子运行 8 环境 × 1000 个控制步， +种子为 123、456、789，模型权重不变。完整参数见 EXP-001。 +历史 JSON 仅记录总转移数 8000，未单独保存环境数和步数；8 × 1000 来自实验命令记录。 +报告脚本核对教师策略、种子、目标命令及总转移数，不能仅凭总转移数证明环境数和步数。 +后续评估应同时保存环境数、控制步数与完整命令，不能给历史 JSON 补造缺失字段。 +加入 `--diagnostics` 才输出各轴速度、各轴 RMSE 与奖励分项。 +统计包含启动和重置过程；下一阶段应提前约定稳态统计窗口，并同时保留全程指标。 +日志若使用 `tee` 保存，会混有启动文字，不是纯 JSON 文件。 + +当前静态配置继承的上游基础奖励定义在 1.3.3 与 1.3.4 有变化。 +历史配置快照应与原运行环境一起用于追溯，不能单独覆盖新版本的全部框架文件。 +重新运行当前分支得到的结果，应另起实验编号，并保存当前 commit 和完整配置。 + +## 训练与 checkpoint 语义 + +3000 次更新 × 64 环境 × 24 rollout 长度 = 4,608,000 次环境转移。 +策略/更新 CUDA 不等于物理仿真也在 GPU；本次物理环境为 CPU。 +当前 `--checkpoint` 只恢复网络权重，未恢复 optimizer/RNG/环境状态，属于 warm-start。 +当前保存行为每 20 次覆盖同一路径；旧运行没有保存全部历史 checkpoint，无法补算其历史速度曲线。 + +## 环境和资产 + +机器人资源通过上游资产流程获取，路径与 home keyframe 必须匹配。 +不要将 `.venv`、mesh、完整运行目录或大模型文件直接加入 Git。 +本分支没有发布训练权重；checkpoint SHA256 用于之后核对同一模型。 +环境安装方式以 fork 上游说明为准；复现旧数据需匹配 provenance 中记录的版本及配置。 diff --git a/docs/rma/THIRD_PARTY_NOTICES.md b/docs/rma/THIRD_PARTY_NOTICES.md new file mode 100644 index 000000000..93021e5ba --- /dev/null +++ b/docs/rma/THIRD_PARTY_NOTICES.md @@ -0,0 +1,14 @@ +# 来源与贡献范围 + +- 上游框架：UniLab，仓库 `https://github.com/unilabsim/UniLab`。 +- 用户 fork：`https://github.com/gongbaojiding-1123/UniLab`。 +- 原有 UniLab 源码、许可证和归属保持不变；许可证见仓库根目录 `LICENSE`。 +- RMA 增量：`rsl_rl/unilab_rma`、RMA 脚本与配置、`deploy_cpp`、相关测试与本组实验文档。 +- `legged_gym/go2_config.py` 只声明接口常量，不是完整 legged_gym 的移植。 +- 当前训练器是本项目独立 PPO；不能将其结果归属于官方 RSL-RL 的默认实现。 +- RMA 的教师隐变量/历史适应思路属于已有研究，本项目不宣称发明该方法。 +- 机器人资源、TensorRT/CUDA、PyTorch 等依赖仍遵循其各自许可，未随实验结果重新分发。 +- 代码和文档使用了 AI 辅助，结果数据来自已保存的训练 CSV 与用户提供的评估输出。 + +原运行目录未携带 Git 历史，无法证明它与某个上游 commit 完全一致。 +本分支保留关键文件差异记录，不通过猜测补填上游 commit。 diff --git a/docs/rma/VALIDATION.md b/docs/rma/VALIDATION.md new file mode 100644 index 000000000..0be17cb15 --- /dev/null +++ b/docs/rma/VALIDATION.md @@ -0,0 +1,64 @@ +# RMA 分支验证记录 + +日期：2026-10-23。 + +本轮新增：47 项 RMA unittest 通过；已有 EXP-003 三组 CSV 成功生成九张图。 +1 环境 × 500 步新增控制诊断与原评估器的 transitions.csv 逐字节一致。 +独立无步进写入检查确认当前 1.3.3 快速路径目标映射错误，普通路径误差为零。 +EXP-004 定点修复后 24 个非零目标映射检查和 500 步真实诊断均为零映射误差； +EXP-005 两组各 1 次更新 CPU 冒烟通过，正式训练尚未进行。最终 `make test-all` 再次运行：ruff/mypy 通过， +仍被未修改框架文件中的 10 个 Pyright 错误、3 个警告阻塞。 + +## 已通过 + +- 导入本地 RMA 增量层及精选实验数据；数据汇总严格匹配 6 份 JSON 和 3000 行训练 CSV。 +- `uv run --no-sync python scripts/report_rma.py`：生成汇总 CSV 与两张静态图；图表已人工查看。 +- `uv run --no-sync python -m unittest discover -s tests/rma -p 'test_*.py'`：41 项通过，包含追踪开关不改变动作、终止状态对齐、控制映射、单变量配置及旧模型防覆盖检查。 +- `cmake -S deploy_cpp -B build/rma -DCMAKE_BUILD_TYPE=Release`：配置成功。 +- `cmake --build build/rma -j2`：构建成功。 +- `ctest --test-dir build/rma --output-on-failure`：3/3 通过（并发一致性、mock 控制和 12 个非零 one-hot 动作的执行器映射）。 +- `make check` 中的 ruff format/check、`mypy src/unilab`：通过。 + +核心测试与报告脚本使用现有 WSL Python 环境。C++ 为 GNU 11.4.0、CMake 生成的 Release 构建。 +没有追加 Go2 长训，没有更改用户运行目录的原模型或基线仿真配置。 +新增独立任务 `rma_forward_no_pose`，只关闭默认站姿偏离惩罚；Hydra 完整解析对比只差这一项。 +在原 1.3.3 运行目录中完成 2 环境 × 24 步 × 1 次更新的 CPU 训练冒烟测试，checkpoint 与日志保存成功。 +随后 2 环境 × 3 步追踪评估成功；惩罚分项为 0，新任务 YAML 已存入快照，原基线模型 SHA256 不变。 +随后用户已完成正式 3000 次更新与三个种子评估，未发现实质前进改善，见 EXP-003 记录。 + +原 1.3.3 运行目录中已执行一次真实追踪评估：`teacher_forward_v1.pt`、种子 123、8 环境 × 1000 步， +生成 8000 行 CSV、独立 JSON 与三张图。全程速度/RMSE 与此前该模型同种子的报告一致。 +逐行验证分项 rate 之和乘 0.02 与实际 reward 的最大误差约 `7.98e-09`。 +完整压缩 CSV、摘要、配置记录及图见 `results/rma/diagnostics/EXP-002/seed123/`。 +这只验证 1.3.3 中的新增评估入口，不等于 1.3.4 真实训练完成兼容。 + +使用旧学生权重重新导出 ONNX，12 组输入数值检查通过，最大误差 `2.682209e-07`。 +导出元数据已区分策略关节顺序与场景执行器存储顺序；C++ 输出边界已按映射散射。 +ONNX 权重图本身未加排列；尚未用真实机器人或 TensorRT 机器人闭环验证本次修正。 + +## 完整门禁尚未通过 + +在本 fork 1.3.4 源码上，复用了已有 1.3.3 的 WSL 环境运行完整门禁： + +```bash +UV_PROJECT_ENVIRONMENT=/home/abc/my_unilab/.venv UV_NO_SYNC=1 make check +UV_PROJECT_ENVIRONMENT=/home/abc/my_unilab/.venv UV_NO_SYNC=1 make test-all +``` + +Pyright 报告上游现有框架文件的 10 项错误和 3 项警告，包括缺失的 packed-reset/capability API、 +未解析导入与可选值检查。报错位置没有被本次改动修改。 +依赖/源码版本不一致是已观察到的限制，尚未证明这是所有错误的唯一原因。 +`make test-all` 因前置类型检查未通过而未到达后续全仓测试阶段，不能报告成全仓测试通过。 +未为通过门禁而升级正在使用的训练环境或更改上游框架代码。 + +若创建 PR，应保持 Draft；本地核心验证不替代完整门禁和当前 head 的远端 CI。 +1.3.4 分支下的真实 Go2 训练、旧 checkpoint 行为兼容、TensorRT/真机与跨引擎测试未在本次执行。 + +## 历史数据边界 + +发布的运动指标是已有本地 1.3.3 运行副本产生的结果，不是当前 1.3.4 分支重跑的结果。 +`results/rma/forward_v1/config_snapshot` 是训练完成后从仍在使用的工作副本采集的配置， +并非训练开始时自动保存的不可变快照；不能据此补造完整历史溯源。 +发布副本的 `base.yaml` 仅清除了行尾空白，未修改 YAML 参数；原运行文件的哈希另有记录。 +原运行配置、CSV 与来源 SHA256 记录见 `results/rma/provenance.json`； +checkpoint SHA256 见 `results/rma/forward_v1/checkpoint_metadata.json`。 diff --git a/experiments/rma/EXP-000_teacher_full_baseline.md b/experiments/rma/EXP-000_teacher_full_baseline.md new file mode 100644 index 000000000..1572cffe3 --- /dev/null +++ b/experiments/rma/EXP-000_teacher_full_baseline.md @@ -0,0 +1,54 @@ +# EXP-000：旧教师固定前进指令评估 + +记录整理：2026-10-22。原始评估目录日期：2026-10-21。 +本记录由已有 JSON 整理，本次没有重新训练或重新评估。 + +## 问题与设置 + +- 模型：旧 `teacher_full.pt`，教师策略；不是新前进配置模型。 +- 目标指令：机体系 `(0.5 m/s, 0 m/s, 0 rad/s)`。 +- 每次 8 环境 × 1000 策略步，共 8000 次环境转移。 +- 三个评估种子：123、456、789。它们不是三个训练种子。 +- 原始 JSON 中未保存完整训练配置、训练 commit 或 checkpoint 校验和；本记录不补造这些信息。 + +## 原始数据 + +实际 WSL 目录： +`/home/abc/my_unilab/runs/rma/circling_diagnosis/verified_20261021_225308/` + +仓库中发布的对应数据： + +- `results/rma/baseline/seed123.json` +- `results/rma/baseline/seed456.json` +- `results/rma/baseline/seed789.json` + +| 评估种子 | 平均 vx (m/s) | 前向 RMSE (m/s) | 平面速度 RMSE (m/s) | 提前终止次数 | +|---|---:|---:|---:|---:| +| 123 | -0.006058 | 0.510957 | 0.514638 | 0 | +| 456 | -0.011667 | 0.517436 | 0.522385 | 1 | +| 789 | -0.004922 | 0.511072 | 0.515623 | 1 | + +表格数值仅为展示做了四舍五入，原始 JSON 保留精度。 +统计包含启动与重置后的过渡过程；不是单独截取的稳态数据。 +提前终止次数不等于经独立接触检测标注的跌倒次数。 + +种子 123 的乘权重、尚未乘控制 dt 的奖励分项： + +- `tracking_lin_vel`：0.360382。 +- `swing_feet_z`：1.272840。 +- `similar_to_default`：-0.183316。 + +## 结论与局限 + +事实：该模型在这三组测试中，平均前向速度接近零，未实现目标速度跟踪。 +辅助摆腿奖励的贡献较高，值得检查是否存在“原地踏步也获得收益”的路径。 +奖励分项大小本身不证明这是唯一原因，也不直接给出哪个奖励权重应设为多少。 +没有由此证明 PPO 公式错误、需要更换仿真器或需要修改部署线程。 + +## 下一步 + +1. 核对后续新模型实际使用的任务与配置。 +2. 用相同固定指令、种子、环境数与步数评估新模型；保留新日志和本基线。 +3. 若仍不前进，按 `EXP-002_diagnosis_plan.md` 设计单因素对照及控制链路诊断。 + +后续前进配置结果现已收集，见 `EXP-001_forward_v1.md`。 diff --git a/experiments/rma/EXP-001_forward_v1.md b/experiments/rma/EXP-001_forward_v1.md new file mode 100644 index 000000000..15b62d76d --- /dev/null +++ b/experiments/rma/EXP-001_forward_v1.md @@ -0,0 +1,49 @@ +# EXP-001：前进配置教师仍未跟踪目标速度 + +日期：2026-10-22。状态：训练与三个种子的评估已完成，运动目标未达成。 + +## 假设与改动 + +原始教师在固定前进指令下几乎不前进。初始假设是辅助摆腿奖励与任务范围可能影响学习。 +本次将 vx 指令收窄到 `[0.3, 0.6]`、vy/wz 置零，速度跟踪权重由 1 改为 2，摆腿奖励权重由 4 改为 0.25。 +这是组合实验，不能将结果归因于单独某一项改动。 + +## 训练证据 + +运行配置记录确认： + +- 教师 PPO，从头训练，初始化种子 42。 +- 环境 64，采样长度 24，更新 3000 次，环境转移 4,608,000 次。 +- 策略/更新设备 CUDA，物理环境设备 CPU，后端 MuJoCo。 +- 模型 `teacher_forward_v1.pt`；训练运行配置、CSV 与 checkpoint 元数据另存于 `results/rma/forward_v1/`。 + +3000 是优化迭代次数，不是环境交互总步数。已完成这一预算，但这不等于证明预算足以收敛。 +每 500 次更新的平均每步奖励约为 0.02093、0.02190、0.02167、0.02149、0.02197、0.02147。 +曲线在早期提高后长期处于平台，没有支持继续无条件加训必然成功的趋势。 + +## 测试协议 + +教师策略、固定机体系指令 `(0.5, 0, 0)`，每种子 8 环境 × 1000 控制步。 +三个评估种子为 123、456、789，模型权重相同。统计包含启动和回合重置后的过渡过程。 + +| 种子 | 平均 vx (m/s) | 前向 RMSE (m/s) | 平面速度 RMSE (m/s) | 提前终止次数 | +|---|---:|---:|---:|---:| +| 123 | 0.015686 | 0.489772 | 0.493154 | 2 | +| 456 | 0.013730 | 0.490668 | 0.493155 | 0 | +| 789 | 0.012254 | 0.491959 | 0.494027 | 0 | + +## 结论 + +- 目标 0.5 m/s 下，三个条件的平均前向速度都只有约 0.014 m/s，尚未学会任务要求的前进。 +- 相比旧模型有小幅变化，但不能把小幅改善描述为已经学会走路。 +- 仅凭最终 checkpoint 不能区分“预算不够”和“局部最优/探索或控制问题”。 +- 降低摆腿奖励并增加跟踪奖励后问题仍存在；不能继续将唯一原因认定为摆腿奖励过强。 +- 原始、新配置的奖励权重不同，总 reward 不可直接横向比较。 +- 三个评估种子不能替代多个训练种子，也不支持统计显著性结论。 + +## 下一步 + +先看已有训练 CSV 的趋势，并设计逐步速度、策略输出范围和控制链路诊断。 +在采样预算相同的前提下做单因素对照，或采用预先约定的延长预算对照。 +当前 `--checkpoint` 只加载权重，没有恢复 optimizer/RNG，不能把它描述为精确续训。 +不自动追加长训、不覆盖原模型，不把失败结果隐藏掉。 diff --git a/experiments/rma/EXP-002_diagnosis_plan.md b/experiments/rma/EXP-002_diagnosis_plan.md new file mode 100644 index 000000000..9083bbc2b --- /dev/null +++ b/experiments/rma/EXP-002_diagnosis_plan.md @@ -0,0 +1,60 @@ +# EXP-002：先诊断平台，再选择对照实验 + +状态：评估追踪工具已实现；不新增训练，不含预测值冒充实测。实际验收范围见验证记录。 + +## 2026-10-22 的第一份诊断数据 + +原 1.3.3 运行目录，保持 `teacher_forward_v1.pt` 权重、任务和固定指令不变： +8 环境 × 1000 步，种子 123，仅增加追踪，得到 8000 条真实转移。 +数据、哈希和图在 [seed123](../../results/rma/diagnostics/EXP-002/seed123/)。 + +- 全程平均 vx `0.01568594 m/s`，前向 RMSE `0.48977159 m/s`，与此前同种子评估一致。 +- 逐回合排除前 100 步后保留 6441 条转移，平均 vx `0.00541195 m/s`，RMSE `0.49605151 m/s`。 +- `FR_hip_joint` 的归一化动作 `|a| >= 0.95` 比例为 `0.749125`；`RR_thigh_joint` 为 `0.952375`。 +- 逐行核对 weighted reward rates × dt 与实际 reward，最大误差约 `7.98e-09`。 + +证据支持：目标指令保持 0.5，速度跟踪很差；部分策略输出长期接近 tanh 边界。 +证据尚不足以证明扭矩饱和、某个奖励项有错、或换一个权重即可学会行走。 +目前只完成这个追踪种子，不把它写成三个诊断种子或新训练的结果。 + +另发现部署侧将策略输出顺序误当成执行器存储顺序；已修正导出元数据和 C++ 输出边界。 +仿真中的策略目标顺序为 FL/FR/RL/RR，场景执行器存储为 FR/FL/RR/RL，UniLab 自己负责映射。 +这不是原 Python 教师不前进的已确认原因，不能把部署修正记为行走性能提升。 + +## 证据 + +EXP-001 已完成 3000 更新、4.608M 环境转移，前向速度约 0.014 m/s。 +训练 CSV 的每 500 更新均值约为 0.02093、0.02190、0.02167、0.02149、0.02197、0.02147。 +约 500 次以后奖励长期平台，现有结果不能支持“继续挂机一定会学会”。 +也不能据此反向断言 4.608M 对所有配置都足够。 + +## 第一项可审查的交付：诊断 + +不改变奖励/控制/优化参数，评估通过 `--trace-dir` 可启用以下记录： + +- 每步、每环境 vx/vy/wz、目标值、done 与回合步数。 +- 每关节归一化动作分布与接近边界的比例；若要定位 PD，另记录目标/实测角和扭矩。 +- 同时报告全程误差与预先定义的启动窗口外误差。 + +验收：不启用记录时动作与原评估一致；记录字段、单位和坐标系明确；重置不跨回合串联。 +当前指标没有提供扭矩饱和证据，不得把“疑似饱和”写成已确认问题。 +数据路径、列含义、画图和 Issue 评论示例见 [操作指南](../../docs/rma/EXPERIMENT_WORKFLOW.md)。 + +## 后续对照候选 + +| 候选 | 保持不变 | 唯一变化 | 目的 | +|---|---|---|---| +| 预算对照 | 配置、初始化种子、环境数、优化设置 | 总训练预算，并保存中间模型 | 检查延长训练是否改善任务指标 | +| 指令消融 | 原奖励、控制器与 PPO | 指令收窄为前进 | 检查任务范围影响 | +| 辅助奖励消融 | 前进指令和其他项 | 摆腿奖励权重 | 检查辅助奖励影响 | +| 跟踪奖励消融 | 其余项 | 速度跟踪权重 | 检查任务激励影响 | + +不同时执行所有候选；先根据诊断选择最有信息量的一项。 +训练预算对照需要连续轨迹或明确标记 warm-start；当前入口不是完整恢复。 +每次保存配置快照、commit、种子、训练总转移数、checkpoint 哈希和相同协议下的评估。 + +## 判断标准 + +主要指标是固定指令下的实际速度与前向 RMSE；侧向/转向误差和提前终止作为辅助指标。 +多个独立训练种子用于检验训练稳定性，不能用三个评估种子替代。 +当前还没有通过指标；任何新增阈值需在实验前约定，并明确是本项目标准。 diff --git a/experiments/rma/EXP-003_no_pose_penalty.md b/experiments/rma/EXP-003_no_pose_penalty.md new file mode 100644 index 000000000..e14fa28bc --- /dev/null +++ b/experiments/rma/EXP-003_no_pose_penalty.md @@ -0,0 +1,86 @@ +# EXP-003：关闭默认站姿偏离惩罚的单变量对照 + +状态：已完成 3000 次更新与三个种子评估，未发现实质前进改善；继续排查控制路径。 + +入口验证：原 1.3.3 运行目录中，2 环境、1 次更新、CPU 冒烟训练已完成并保存日志和模型。 +对应文件 `runs/rma/smoke_no_pose_20261022_2305.pt`，日志目录 +`runs/rma/smoke_no_pose_20261022_2305_logs/20261022_230512_v5z9y6n2/`。 +这 48 条训练转移用于检查链路，不作为正式对照组或行走性能证据。 +随后用同一实验任务完成 2 环境 × 3 步评估，惩罚分项为 0，原基线模型 SHA256 不变。 + +## 假设与唯一变化 + +问题：已有 `teacher_forward_v1.pt` 在 0.5 m/s 指令下几乎不前进。 +假设：默认站姿偏离惩罚可能抑制学习有效步态；这是待检验的解释，不是已确认根因。 + +基线任务 `go2_joystick_flat/rma_forward` 的 `reward.similar_to_default.weight` 为 -0.1。 +实验任务 `go2_joystick_flat/rma_forward_no_pose` 继承基线，只将该值改为 0.0。 +Hydra 完整解析后的配置测试要求除这一项外完全一致。 +基线配置文件保持原值；不要为了本实验直接修改 `base.yaml`。 + +## 训练协议 + +从头训练，初始化种子 42，64 环境，采样长度 24，更新 3000 次，总转移 4,608,000。 +与基线使用同一设备设置 CUDA，其余 PPO、网络、控制、随机化与指令设置不变。 +不传 `--checkpoint`；该选项会加载旧权重，改变实验起点。 +当前入口也不恢复 optimizer/RNG，因此不能把加载权重称为精确续训。 + +在原可运行 WSL 目录执行： + +```bash +cd /home/abc/my_unilab +uv run --no-sync python scripts/train_rma.py \ + --phase teacher \ + --task go2_joystick_flat/rma_forward_no_pose \ + --output runs/rma/teacher_forward_no_pose_v1.pt \ + --num-envs 64 --iterations 3000 --device cuda +``` + +原基线：`/home/abc/my_unilab/runs/rma/teacher_forward_v1.pt`。 +新模型：`/home/abc/my_unilab/runs/rma/teacher_forward_no_pose_v1.pt`。 +新训练日志：`/home/abc/my_unilab/runs/rma/teacher_forward_no_pose_v1_logs//`， +包含 `metrics.csv`、`run_config.json` 与 `tensorboard/`。 +入口会拒绝已经存在的输出路径；重复正式实验请使用新文件名。 +同一次训练中周期性更新本次的新 checkpoint 属于正常保存行为。 + +## 评估协议 + +正式训练完成后再执行；评估同样选择实验任务，确保环境与奖励配置对应。 + +```bash +for seed in 123 456 789; do + uv run --no-sync python scripts/evaluate_rma.py \ + --policy teacher \ + --checkpoint runs/rma/teacher_forward_no_pose_v1.pt \ + --task go2_joystick_flat/rma_forward_no_pose \ + --num-envs 8 --steps 1000 --seed "$seed" \ + --command 0.5 0 0 --diagnostics \ + --trace-dir "runs/rma/evaluations/EXP-003/seed${seed}" || break +done +``` + +主要指标是平均前向速度与前向 RMSE；侧向/转向误差、提前终止及动作范围辅助判断。 +两组总奖励不可直接比较：取消负项，即使运动不变也可能提高 reward。 +第一轮只匹配基线的一个训练种子，结论限于初步诊断；多个评估种子不能替代独立训练种子。 +若无改善应记录结果，不同时再调整动作缩放或训练预算。 + +## 实测结果（2026-10-23） + +原始评估目录：`runs/rma/evaluations/EXP-003/20261023_151848/`，每种子 8000 转移。 + +| 评估种子 | 平均 vx (m/s) | vx RMSE (m/s) | 提前终止 | +|---|---:|---:|---:| +| 123 | 0.01392659 | 0.48912616 | 0 | +| 456 | 0.01327861 | 0.48960597 | 0 | +| 789 | 0.01269397 | 0.49064972 | 0 | + +与原前进教师约 0.012—0.016 m/s 相近，取消姿态惩罚未带来实质前进。 +这只是一个训练种子的对照；不能据此证明该奖励在所有条件下无效。 +三组图已由原始 CSV 生成，每个 eval 目录下的 plots 含速度、动作、奖励三图。 + +后续 1 环境 × 500 步控制诊断（`runs/rma/control_diagnostics/EXP-003/eval_2ffou4yp`） +目标映射残差最大 0.47117 rad。当前运行副本快速路径直接复制自然关节顺序目标， +忽略不同的执行器顺序。无步进非零目标检查：快速写入最大误差 0.0300000 rad， +普通写入为零。该问题是当前控制链路缺陷的证据，不代表已解释全部行走失败。 +本轮未改控制器、未改奖励或重训。先修复并验收目标写入，再另起实验建立新基线。 +当前 public backend 不提供实测执行器扭矩，代数 PD 需求估计不能证明扭矩饱和。 diff --git a/experiments/rma/EXP-004_control_mapping.md b/experiments/rma/EXP-004_control_mapping.md new file mode 100644 index 000000000..9dd688b13 --- /dev/null +++ b/experiments/rma/EXP-004_control_mapping.md @@ -0,0 +1,49 @@ +# EXP-004：修复目标写入，重建训练基线 + +状态：1.3.3 运行副本已修复目标映射，真实短诊断通过；正式训练尚未执行。 + +## 问题与范围 + +自然关节顺序为 FL/FR/RL/RR，后端执行器存储顺序为 FR/FL/RR/RL。 +原 `Entity.set_full_width_joint_position_target` 直接 `control.copy_(target)`， +未做重排。普通 `set_joint_position_target` 已正确解析关节到执行器。 + +把 GitHub 1.3.4 已有的 full-width 映射实现与懒加载索引缓存定点移植到当前 +`/home/abc/my_unilab/src/unilab/base/entity.py`；没有覆盖整个框架或升级依赖。 +源补丁见 [mapping.patch](patches/unilab_1_3_3_full_width_mapping.patch)。 +该实现缓存解析后的索引，没有每步读取 XML、文件或 CPU 往返； +正确的重排有计算开销，本轮未做吞吐基准，不承诺与错误直接复制路径完全同速。 + +原文件备份：`runs/rma/backups/EXP-004_mapping_20261023_171400/entity.py`。 +修改前 SHA256：`546487c77ea7ab8e288ac63eb7dddea08fad6de845f256a436e2151724b7e1d6`。 +修改后 SHA256：`54f5126f62e71acfce6c87c051fbc5e2411264f396dd39161bb7376556810d4d`。 +这些是运行副本的文件哈希，不是 GitHub 1.3.4 文件哈希。 + +## 验收 + +无物理步进的不同非零目标检查：快速/普通路径映射最大误差现均为 0（修复前快速路径为 0.0300000 rad）。 +同一个旧教师、种子 123、500 步的真实控制诊断： +`runs/rma/control_diagnostics/EXP-004_mapping/eval_k8559ph8`， +`max_abs_mapping_residual_rad = 0.0`（修复前为 0.47117）。 +该检查读取实际控制缓冲，不通过修改观测顺序掩盖错误。 + +旧策略是在错误映射下训练的，修复后直接播放的表现不能代表修复后训练的效果。 +没有以旧模型不跌倒或突然走起来作为本修复的验收标准。 +两组新初始化的教师入口冒烟测试见 EXP-005；它们不构成学习成功证据。 + +## 下一步只做这件事 + +从头训练修复后的原前进任务，保留 pose 惩罚 -0.1、std 0.5、scale 0.25、 +初始化种子 42、64 环境、3000 次更新，不加载旧 checkpoint。 +这次是工程修复后的新基线，不与 EXP-003 混为一个奖励对照。 + +```bash +cd /home/abc/my_unilab +uv run --no-sync python scripts/train_rma.py \ + --phase teacher --task go2_joystick_flat/rma_forward \ + --output runs/rma/teacher_mapping_v1.pt \ + --num-envs 64 --iterations 3000 --device cuda +``` + +评估与后续 std 实验的完整命令见 [EXP-005](EXP-005_tracking_width.md)。 +大训练仍由用户自行启动；本轮仅执行短检查，没有启动后台长训。 diff --git a/experiments/rma/EXP-005_tracking_width.md b/experiments/rma/EXP-005_tracking_width.md new file mode 100644 index 000000000..b9207a87e --- /dev/null +++ b/experiments/rma/EXP-005_tracking_width.md @@ -0,0 +1,158 @@ +# EXP-005：只收紧前向速度跟踪宽度 + +状态：配置已安装到当前运行副本，单变量解析检查与短训练入口已验证； +两组正式 3000 次更新尚未执行，尚无性能结论。 + +## 先理解这个变量 + +把奖励理解成“速度答卷的评分标准”：偏离目标多少，就扣掉多少分。 +当前 1.3.3 实现 `track_lin_vel_xy_exp` 返回： + +`r_tracking = exp(-((vx_command-vx)^2 + (vy_command-vy)^2) / std^2)`。 + +实验仅将 std 从 0.5 改为 0.35 m/s，weight 保持 2.0。 +下面是尚未乘 weight/dt 的单个分项，假设 vy 误差为 0： + +| vx 误差 (m/s) | std=0.5 | std=0.35 | +|---|---:|---:| +| 0 | 1.0000 | 1.0000 | +| 0.1 | 0.9608 | 0.9216 | +| 0.2 | 0.8521 | 0.7214 | +| 0.5 | 0.3679 | 0.1299 | + +候选假设：更严格的速度奖励能改善跟踪。 +相反结果也可能发生：误差大时奖励很小，策略更难获得有区分度的信号。 +所以 std 小不是天然更好。这不是 PPO 的动作采样标准差，也不是动作缩放或奖励权重。 + +## 前置条件与两组定义 + +两组均使用 EXP-004 修复后的相同源码，从头初始化、种子 42、64 环境 × 24 rollout × +3000 updates = 4,608,000 transitions，设备均 CUDA（物理仍为 CPU MuJoCo）。 +基线重新使用原前进任务，pose 惩罚为 -0.1；不继承 EXP-003 的 no_pose 配置。 +本次不叠加新的奖励权重、学习率、scale 或训练预算调整。 + +| 组 | task | std | 新输出文件 | +|---|---|---:|---| +| 修复后基线 | go2_joystick_flat/rma_forward | 0.5 | teacher_mapping_v1.pt | +| 收紧宽度 | go2_joystick_flat/rma_forward_tight_tracking | 0.35 | teacher_mapping_std035_v1.pt | + +旧的 teacher_forward_v1 / teacher_forward_no_pose_v1 在错误映射下训练， +不作为本次 std 对照的基线。不要通过加载旧权重伪装成从头训练。 +一个训练种子的结果只用于初步比较，三个评估种子不是三次独立训练。 + +## 到底改哪个文件 + +当前训练机器的文件： +`/home/abc/my_unilab/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_tight_tracking.yaml`。 + +```yaml +# @package _global_ +defaults: + - /task/go2_joystick_flat/rma_forward + - _self_ + +reward: + tracking_lin_vel: + params: + std: 0.35 +``` + +`defaults` 继承基线，`_self_` 让本文件最后覆盖它。 +只写 std，其他参数继承；不要修改 base.yaml，也不要在 train_rma.py 中改 reward。 +训练和评估通过 `--task` 选择配置；改 YAML 不会修改已保存网络的行为。 + +以后学习修改时，在 WSL 执行： + +```bash +nano /home/abc/my_unilab/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_tight_tracking.yaml +``` + +只修改 `std: 0.35` 这一行；Ctrl+O、Enter 保存，Ctrl+X 退出。 +本轮先保持 0.35 不变。下一轮若换成 0.4，先保存旧配置、另起实验编号和模型名， +不能悄悄修改本轮协议后继续把结果标成 EXP-005。标准实验配置和命令应提交 Git。 + +### 验证有没有误改第二个变量 + +```bash +cd /home/abc/my_unilab +uv run --no-sync python scripts/inspect_rma_task.py \ + --task go2_joystick_flat/rma_forward_tight_tracking \ + --compare-with go2_joystick_flat/rma_forward \ + --expect-only reward.tracking_lin_vel.params.std +``` + +应看到 changes 只有 std：before=0.5、after=0.35。多改了一项或完全没改都会返回非零状态。 +这个命令不训练、不创建物理环境；只解析实际 Hydra 配置。 +已实测的比较结果保存在 `runs/rma/experiments/EXP-005/config_comparison.json`。 + +## 训练：两组依次运行，不同时占用电脑 + +1. 先运行 EXP-004 的修复后基线训练，并完成下面评估。 +2. 基线有明确评估记录后，仍想测试这个假设，再运行实验组。 + +```bash +cd /home/abc/my_unilab +uv run --no-sync python scripts/train_rma.py \ + --phase teacher --task go2_joystick_flat/rma_forward_tight_tracking \ + --output runs/rma/teacher_mapping_std035_v1.pt \ + --num-envs 64 --iterations 3000 --device cuda +``` + +两组正式训练都不加 `--checkpoint`，均从头训练。输出已存在时脚本会拒绝覆盖； +重做实验应明确改输出名，不能删除旧模型来掩盖重跑。 +日志分别在 `teacher_mapping_v1_logs//` 与 +`teacher_mapping_std035_v1_logs//`，包括 metrics.csv / tensorboard。 +不要在高温下继续长训；本轮没有启动这两组长训练。 + +## 每组训练完成后：用共同任务评估 + +为避免不同奖励定义误导总 reward，正式比较统一在原前进任务评估： +即实验组网络也选择 `--task go2_joystick_flat/rma_forward`。 +这种做法不会改变网络，且两组的评估奖励、指令、动力学一致。 +主要比较实测速度与 RMSE，而非只比较各自的训练 reward。 + +基线训练结束后执行；实验组训练完再把 model 一行改为 `teacher_mapping_std035_v1`： + +```bash +cd /home/abc/my_unilab +model=teacher_mapping_v1 +for seed in 123 456 789; do + uv run --no-sync python scripts/evaluate_rma.py \ + --policy teacher --checkpoint "runs/rma/${model}.pt" \ + --task go2_joystick_flat/rma_forward \ + --num-envs 8 --steps 1000 --seed "$seed" \ + --command 0.5 0 0 --diagnostics \ + --trace-dir "runs/rma/evaluations/EXP-005/${model}/seed${seed}" || break +done +``` + +结果：终端 JSON + 每次独立 trace 目录中的 summary.json / transitions.csv / run_config.json。 +逐项比较 `diagnostics.mean_measured_velocity[0]`、 +`diagnostics.tracking_rmse_by_axis[0]`、侧向/转向误差及提前终止。 +`terminations` 不是独立测量的跌倒率。 +每回合排除前 100 步的 trace 窗口另行对照，同时保留全程指标。 +训练任务源码应在改动前存档；trace 快照表示评估时配置，不能用它倒推以前训练配置。 + +画图：对评估输出的实际 trace.directory 执行： + +```bash +uv run --no-sync python scripts/plot_evaluation_trace.py /实际的/trace.directory +``` + +## 两个版本不要混用 + +本记录和上述命令针对当前 1.3.3 的 `std`，运行副本安装的源文件来自 +`experiments/rma/runtime_1_3_3/rma_forward_tight_tracking.yaml`。 +GitHub 1.3.4 的同名任务使用 `tracking_sigma`，公式为 exp(-error/tracking_sigma)： +新宽度 0.35 对应分母 0.1225，且该版本的原基线分母是 0.5，等效 std 约 0.7071。 +因此 1.3.4 的 .5 → .1225 与本记录的 std .5 → .35 不是同一个基线变化； +1.3.4 测试仅保护该版本单字段变更，不算本轮运行数据。 +不要把两个版本完整覆盖或混为同一实验。新版本需独立编号、重新建基线。 + +## 本轮已经执行与尚未执行 + +- 已修复并备份控制映射，残差验证为 0。 +- 已解析实际 1.3.3 配置，确认仅 std .5 → .35；reward weight 等其他项一致。 +- 两组各 2 环境 × 1 次更新的 CPU 短训练用于接口验证，模型名为 + `smoke_mapping_baseline_20261023.pt` 与 `smoke_tight_std_20261023.pt`。 +- 正式新基线和 std 对照未运行，没有效果结论；不预填成功数值。 diff --git a/experiments/rma/EXP-006_sim2sim_nominal.md b/experiments/rma/EXP-006_sim2sim_nominal.md new file mode 100644 index 000000000..6132ea860 --- /dev/null +++ b/experiments/rma/EXP-006_sim2sim_nominal.md @@ -0,0 +1,72 @@ +# EXP-006：冻结学生策略的 MuJoCo / Genesis 对照 + +- 日期：2026-10-24。 +- 状态：完成单环境、单种子、1000 步的名义条件评估；仍需诊断速度波动。 +- 问题：Go2 在 Genesis 可以前进，但肉眼观察到踉跄。 +- 目标：先核对观测、控制与 reset 约定，再比较两个物理引擎的速度跟踪。 +- 模型：`student_mapping_v1_500.pt` 与对应 ONNX；本实验不训练、不改变权重。 +- 本次是接口修正后的对照，不是单变量奖励消融。多项接口修正同时存在，不能将结果归因于其中一项。 + +## 协议与需要保留的约定 + +两端使用 `--sim2sim-nominal`：home 初始姿态、零初始速度、观测噪声关闭， +机器人滑动摩擦系数 1.0、附加负载 0 kg、执行器 kp=35、kd=0； +被动关节阻尼 0.5 与执行器 kd 是不同参数。控制周期 0.02 s。 +单环境，seed=123，1000 步，指令为前向 0.5 m/s、侧向 0、偏航角速度 0。 +MuJoCo 初始回合计数清零；两端本次均在第 1000 步超时。 + +既有模型的重力输入来自负的 MJCF IMU site `framezaxis`，即世界坐标表达的 site z 轴取负。 +本次 Genesis 保持该训练定义；不能直接替换成常见的机体坐标重力投影，否则改变冻结模型输入。 +速度比较均使用动作执行后的 IMU 位置速度，Genesis 包含 IMU 偏移的角速度叉乘修正。 + +## 结果 + +来源：[comparison.json](../../results/rma/sim2sim/EXP-006/comparison.json)。 +RMSE 按全程逐步误差计算，不是平均速度与指令之差。 + +| 指标 | MuJoCo | Genesis | +| --- | ---: | ---: | +| 平均前向速度，m/s | 0.552057 | 0.486771 | +| 前向速度 RMSE，m/s | 0.101833 | 0.165540 | +| 平面速度 RMSE，m/s | 0.112781 | 0.174903 | +| 平均侧向速度，m/s | 0.023105 | 0.027043 | +| 平均偏航角速度，rad/s | -0.086434 | 0.038306 | +| 跌倒或异常终止 | 0 | 0 | +| 右后小腿动作接近边界比例 | 41.7% | 44.9% | + +### 速度跟踪 + +![速度对照](../../results/rma/sim2sim/EXP-006/plots/velocity_comparison.png) + +Genesis 平均前向速度接近指令，但约 3 s、9.5–10 s、11.5–12 s 存在明显降速。 +平均达标不能代替逐步跟踪和 RMSE 检查。 + +### 动作分布 + +![动作边界比例](../../results/rma/sim2sim/EXP-006/plots/action_near_bounds.png) + +这里统计归一化动作 `|action| >= 0.95` 的比例，不能据此宣称机械限位或电机力矩饱和。 +右后小腿是值得进一步检查的关节，但现有数据不能证明它导致降速。 + +### 姿态与高度 + +![Genesis 稳定性](../../results/rma/sim2sim/EXP-006/plots/genesis_stability.png) + +本次没有跌倒，但存在姿态与高度起伏。 + +## 结论与边界 + +支持：冻结策略已在另一物理引擎完成一次前进闭环，当前 Genesis 速度跟踪误差比 MuJoCo 大。 +不支持：已证明任意初始状态下鲁棒、已定位踉跄根因、已达到真机部署验收。 +home reset 且无噪声时，更换随机种子大多重复同一条件；不能把它当作充分的鲁棒性测试。 +接触求解与积分仍有引擎差异，不能宣称两端动力学完全一致。 + +## 下一项最小工作 + +保持模型、奖励和控制参数不变，优先检查降速时段的足端接触、关节目标/实际角度、 +执行器力矩及限幅。区分后端实际读数和 PD 代数估计，再决定是否开展单变量实验。 + +本次完整原始数据保留在 WSL： +`/home/abc/my_unilab/runs/rma/evaluations/sim2sim_nominal_v2_seed123`。 +GitHub 精选证据、来源说明与复查方式见 +[结果目录](../../results/rma/sim2sim/EXP-006/README.md)。 diff --git a/experiments/rma/TEMPLATE.md b/experiments/rma/TEMPLATE.md new file mode 100644 index 000000000..b78b99c89 --- /dev/null +++ b/experiments/rma/TEMPLATE.md @@ -0,0 +1,15 @@ +# 实验编号： + +- 日期 / 状态： +- 问题与已有证据： +- 可被推翻的假设： +- 相对哪个基线，只改变哪一项： +- 代码 commit / 未提交补丁： +- 完整配置快照 / 依赖信息： +- 训练命令 / 初始化种子 / 总环境转移数 / 设备： +- 日志目录 / checkpoint / SHA256： +- 评估指令 / 种子 / 环境数 / 步数 / 统计窗口： +- 实验前约定的主要指标： +- 逐种子结果 / 原始数据 / 图表 / 视频： +- 结论支持什么、不支持什么： +- 下一项最小实验： diff --git a/experiments/rma/patches/unilab_1_3_3_full_width_mapping.patch b/experiments/rma/patches/unilab_1_3_3_full_width_mapping.patch new file mode 100644 index 000000000..880bfd5e6 --- /dev/null +++ b/experiments/rma/patches/unilab_1_3_3_full_width_mapping.patch @@ -0,0 +1,80 @@ +--- a/src/unilab/base/entity.py ++++ b/src/unilab/base/entity.py +@@ -1515,6 +1515,11 @@ + self._actuator_target_joint_names, + self._joint_to_actuator_local, + ) = self._materialize_joint_actuator_mapping(backend, actuator_ids) ++ self._full_width_control_index_host: np.ndarray | None = None ++ self._full_width_natural_index_host: np.ndarray | None = None ++ self._full_width_control_index_tensor: torch.Tensor | None = None ++ self._full_width_natural_index_tensor: torch.Tensor | None = None ++ self._full_width_index_device: torch.device | None = None + if control_buffer is not None: + expected_control_shape = (backend.num_envs, backend.num_actuators) + if control_buffer.shape != expected_control_shape: +@@ -2519,19 +2524,60 @@ + f"Entity '{self.name}' capability 'joint position target' is unavailable " + f"for passive joints on backend '{self._backend_type}': {passive_names}" + ) +- control = self._require_tensor_control_buffer_public() + if target.dtype != torch.float32: + raise TypeError("full-width joint position target must be float32") ++ control = self._require_tensor_control_buffer_public() + if target.device != control.device: + raise ValueError( + f"full-width joint position target must live on {control.device}, got {target.device}" + ) +- if tuple(target.shape) != tuple(control.shape): ++ if tuple(target.shape) != (control.shape[0], joint_count): + raise ValueError( + f"full-width joint position target has shape {tuple(target.shape)}; " +- f"expected {tuple(control.shape)}" +- ) +- control.copy_(target) ++ f"expected {(control.shape[0], joint_count)} in natural joint order" ++ ) ++ if self._actuator_ids is None: ++ raise self._capability_error( ++ "actuator control write", "actuator_names were not declared in EntityCfg" ++ ) ++ control_index, natural_index = self._full_width_control_indices( ++ control, joint_to_actuator, joint_count ++ ) ++ control[:, control_index] = target.index_select(1, natural_index) ++ ++ ++ def _full_width_control_indices( ++ self, ++ control: torch.Tensor, ++ joint_to_actuator: np.ndarray, ++ joint_count: int, ++ ) -> tuple[torch.Tensor, torch.Tensor]: ++ assert self._actuator_ids is not None ++ if self._full_width_control_index_host is None: ++ backend_to_natural = np.full(control.shape[1], -1, dtype=np.intp) ++ backend_to_natural[self._actuator_ids[joint_to_actuator]] = np.arange( ++ joint_count, dtype=np.intp ++ ) ++ self._full_width_control_index_host = np.flatnonzero(backend_to_natural >= 0) ++ self._full_width_natural_index_host = backend_to_natural[ ++ self._full_width_control_index_host ++ ] ++ if ( ++ self._full_width_control_index_tensor is None ++ or self._full_width_natural_index_tensor is None ++ or self._full_width_index_device != control.device ++ ): ++ self._full_width_control_index_tensor = torch.from_numpy( ++ self._full_width_control_index_host ++ ).to(device=control.device) ++ self._full_width_natural_index_tensor = torch.from_numpy( ++ self._full_width_natural_index_host ++ ).to(device=control.device) ++ self._full_width_index_device = control.device ++ return ( ++ self._full_width_control_index_tensor, ++ self._full_width_natural_index_tensor, ++ ) + + def _require_tensor_control_buffer_public(self) -> torch.Tensor: + control = self.data.control_buffer diff --git a/experiments/rma/runtime_1_3_3/rma_forward_tight_tracking.yaml b/experiments/rma/runtime_1_3_3/rma_forward_tight_tracking.yaml new file mode 100644 index 000000000..9b1bbc63a --- /dev/null +++ b/experiments/rma/runtime_1_3_3/rma_forward_tight_tracking.yaml @@ -0,0 +1,11 @@ +# @package _global_ +# EXP-005: working 1.3.3 runtime ONLY. Baseline std=0.5 -> std=0.35. +# Inherit rma_forward, not rma_forward_no_pose: keep pose penalty at -0.1. +defaults: + - /task/go2_joystick_flat/rma_forward + - _self_ + +reward: + tracking_lin_vel: + params: + std: 0.35 diff --git a/legged_gym/go2_config.py b/legged_gym/go2_config.py new file mode 100644 index 000000000..e17cc1ccb --- /dev/null +++ b/legged_gym/go2_config.py @@ -0,0 +1,10 @@ +"""RMA contract only; canonical simulation config remains in unilab/conf YAML.""" + +OBS_DIM = 49 +CRITIC_DIM = 52 +PRIVILEGED_DIM = 2 +HISTORY_LENGTH = 50 +LATENT_DIM = 8 +NUM_ACTIONS = 12 +POLICY_PERIOD_S = 0.02 +ACTION_SCALE = 0.25 diff --git a/requirements-rma-export.txt b/requirements-rma-export.txt new file mode 100644 index 000000000..baf3d05bc --- /dev/null +++ b/requirements-rma-export.txt @@ -0,0 +1,4 @@ +# Install inside the original, working UniLab environment. +onnx>=1.16,<2 +onnxruntime<1.20; python_version < '3.11' +onnxruntime>=1.20; python_version >= '3.11' diff --git a/results/rma/baseline/seed123.json b/results/rma/baseline/seed123.json new file mode 100644 index 000000000..446ec94a6 --- /dev/null +++ b/results/rma/baseline/seed123.json @@ -0,0 +1,61 @@ +{ + "policy": "teacher", + "seed": 123, + "completed_episodes": 8, + "mean_completed_episode_return": 22.94372197985649, + "mean_completed_episode_length_steps": 636.25, + "mean_step_reward": 0.03588731563591864, + "planar_velocity_rmse_m_per_s": 0.5146384602254557, + "unfinished_episodes": 8, + "mean_unfinished_episode_return": 12.943596839904785, + "student_latent_mse_vs_teacher": null, + "student_action_rmse_vs_teacher": null, + "terminations": 0, + "total_transitions": 8000, + "command_override": [ + 0.5, + 0.0, + 0.0 + ], + "diagnostics": { + "velocity_axes": [ + "vx_m_per_s", + "vy_m_per_s", + "yaw_rate_rad_per_s" + ], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": [ + 0.5, + 0.0, + 0.0 + ], + "mean_command": [ + 0.5, + 0.0, + 0.0 + ], + "mean_measured_velocity": [ + -0.006058229419679051, + 0.005980759545039106, + 0.014841998879314587 + ], + "tracking_rmse_by_axis": [ + 0.5109570487642697, + 0.061446229894282584, + 0.11541315228345941 + ], + "mean_absolute_yaw_rate_rad_per_s": 0.06477700540811179, + "weighted_reward_terms_before_dt": { + "tracking_lin_vel": 0.3603822733908892, + "tracking_ang_vel": 0.1920580652654171, + "lin_vel_z": -0.011202149931981695, + "ang_vel_xy": -0.023651930230640574, + "base_height": -0.004054067645105533, + "action_rate": -0.008745710445419946, + "similar_to_default": -0.18331554384529591, + "contact": 0.2000549937337637, + "swing_feet_z": 1.2728398969173431 + }, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards" + } +} diff --git a/results/rma/baseline/seed456.json b/results/rma/baseline/seed456.json new file mode 100644 index 000000000..3b134fb90 --- /dev/null +++ b/results/rma/baseline/seed456.json @@ -0,0 +1,61 @@ +{ + "policy": "teacher", + "seed": 456, + "completed_episodes": 9, + "mean_completed_episode_return": 17.958606203397114, + "mean_completed_episode_length_steps": 509.6666666666667, + "mean_step_reward": 0.035673840930336154, + "planar_velocity_rmse_m_per_s": 0.5223854549035621, + "unfinished_episodes": 8, + "mean_unfinished_episode_return": 15.470402717590332, + "student_latent_mse_vs_teacher": null, + "student_action_rmse_vs_teacher": null, + "terminations": 1, + "total_transitions": 8000, + "command_override": [ + 0.5, + 0.0, + 0.0 + ], + "diagnostics": { + "velocity_axes": [ + "vx_m_per_s", + "vy_m_per_s", + "yaw_rate_rad_per_s" + ], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": [ + 0.5, + 0.0, + 0.0 + ], + "mean_command": [ + 0.5, + 0.0, + 0.0 + ], + "mean_measured_velocity": [ + -0.011666606367917325, + 0.014149421059863528, + 0.018934832205886893 + ], + "tracking_rmse_by_axis": [ + 0.5174359250308406, + 0.07174000046007187, + 0.143143680448307 + ], + "mean_absolute_yaw_rate_rad_per_s": 0.06684632264653795, + "weighted_reward_terms_before_dt": { + "tracking_lin_vel": 0.3547401953712106, + "tracking_ang_vel": 0.19153448170423507, + "lin_vel_z": -0.014624936374981188, + "ang_vel_xy": -0.025369600676742265, + "base_height": -0.006269701974932105, + "action_rate": -0.008777687560716459, + "similar_to_default": -0.18115082198753954, + "contact": 0.20040749409794809, + "swing_feet_z": 1.2732026720046996 + }, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards" + } +} diff --git a/results/rma/baseline/seed789.json b/results/rma/baseline/seed789.json new file mode 100644 index 000000000..6706f4473 --- /dev/null +++ b/results/rma/baseline/seed789.json @@ -0,0 +1,61 @@ +{ + "policy": "teacher", + "seed": 789, + "completed_episodes": 8, + "mean_completed_episode_return": 17.56580749154091, + "mean_completed_episode_length_steps": 496.875, + "mean_step_reward": 0.03569934609872871, + "planar_velocity_rmse_m_per_s": 0.5156226711943084, + "unfinished_episodes": 8, + "mean_unfinished_episode_return": 18.13353729248047, + "student_latent_mse_vs_teacher": null, + "student_action_rmse_vs_teacher": null, + "terminations": 1, + "total_transitions": 8000, + "command_override": [ + 0.5, + 0.0, + 0.0 + ], + "diagnostics": { + "velocity_axes": [ + "vx_m_per_s", + "vy_m_per_s", + "yaw_rate_rad_per_s" + ], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": [ + 0.5, + 0.0, + 0.0 + ], + "mean_command": [ + 0.5, + 0.0, + 0.0 + ], + "mean_measured_velocity": [ + -0.004921973013823731, + 0.005795657981586174, + -0.0007342302818937725 + ], + "tracking_rmse_by_axis": [ + 0.5110722221933418, + 0.06835146357993736, + 0.15672107160305368 + ], + "mean_absolute_yaw_rate_rad_per_s": 0.07771462473009702, + "weighted_reward_terms_before_dt": { + "tracking_lin_vel": 0.36330923438072205, + "tracking_ang_vel": 0.1901205448284745, + "lin_vel_z": -0.013487572537051164, + "ang_vel_xy": -0.028084615291387307, + "base_height": -0.004377204851363786, + "action_rate": -0.008941495961428927, + "similar_to_default": -0.18495080276578665, + "contact": 0.19976249381899833, + "swing_feet_z": 1.2716167651414871 + }, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards" + } +} diff --git a/results/rma/diagnostics/EXP-002/seed123/plots/action_distribution.png b/results/rma/diagnostics/EXP-002/seed123/plots/action_distribution.png new file mode 100644 index 000000000..0916f7887 Binary files /dev/null and b/results/rma/diagnostics/EXP-002/seed123/plots/action_distribution.png differ diff --git a/results/rma/diagnostics/EXP-002/seed123/plots/reward_components.png b/results/rma/diagnostics/EXP-002/seed123/plots/reward_components.png new file mode 100644 index 000000000..1e0b7b634 Binary files /dev/null and b/results/rma/diagnostics/EXP-002/seed123/plots/reward_components.png differ diff --git a/results/rma/diagnostics/EXP-002/seed123/plots/velocity_tracking.png b/results/rma/diagnostics/EXP-002/seed123/plots/velocity_tracking.png new file mode 100644 index 000000000..ce2d9e86a Binary files /dev/null and b/results/rma/diagnostics/EXP-002/seed123/plots/velocity_tracking.png differ diff --git a/results/rma/diagnostics/EXP-002/seed123/provenance.json b/results/rma/diagnostics/EXP-002/seed123/provenance.json new file mode 100644 index 000000000..2d397f158 --- /dev/null +++ b/results/rma/diagnostics/EXP-002/seed123/provenance.json @@ -0,0 +1,12 @@ +{ + "evaluation_only": true, + "source_runtime": "existing local UniLab 1.3.3, NOT the 1.3.4 fork runtime", + "source_trace_directory": "/home/abc/my_unilab/runs/rma/evaluations/EXP-002/seed123/eval_hhlzyanz", + "rows": 8000, + "uncompressed_transitions_sha256": "cb49a7842ac278a026e50b68e32ded5a5049a4906388d5c93d071c9d5a52798b", + "source_run_config_sha256": "218ec48c5d484046f280ac4588f688527ec37ba201212f9d5ad84ea898e50a06", + "summary_directory_normalization": "absolute trace.directory made relative to source runtime", + "per_transition_max_abs_reward_minus_sum_weighted_rates_times_dt": 7.981434464732207e-09, + "mapping_note": "runtime target order FL FR RL RR; scene storage order FR FL RR RL", + "validation_scope": "one evaluation seed; no new training or real robot/TensorRT control execution" +} diff --git a/results/rma/diagnostics/EXP-002/seed123/run_configuration.json b/results/rma/diagnostics/EXP-002/seed123/run_configuration.json new file mode 100644 index 000000000..5bcc66452 --- /dev/null +++ b/results/rma/diagnostics/EXP-002/seed123/run_configuration.json @@ -0,0 +1,207 @@ +{ + "format": "unilab-rma-evaluation-trace-v1", + "created_at_utc": "2026-10-22T14:05:09.081075+00:00", + "arguments": { + "checkpoint": "runs/rma/teacher_forward_v1.pt", + "policy": "teacher", + "steps": 1000, + "num_envs": 8, + "seed": 123, + "device": "cpu", + "task": "go2_joystick_flat/rma_forward", + "command": [ + 0.5, + 0.0, + 0.0 + ], + "diagnostics": true, + "trace_dir": "runs/rma/evaluations/EXP-002/seed123", + "trace_warmup_steps": 100, + "backend": "pytorch", + "engine": null, + "tensorrt_worker": "/home/abc/my_unilab/build/deploy_trt/policy_worker", + "render": false, + "render_speed": 1.0 + }, + "git_commit": null, + "git_dirty": null, + "checkpoint_sha256": "5454f899a358932d7208aaf52e82937e1db51f3f71609f1a79204bcc2f6a8fb9", + "control_period_s": 0.02, + "warmup_steps_per_episode": 100, + "velocity_frame": "body; clean critic [49:51] and gyro z [2]", + "transition_alignment": "before = s_t; action = a_t; after = terminal s_(t+1), before any reset. Commands at both endpoints are retained; reward belongs to this transition. Full/window summaries use before velocity and command, matching --diagnostics.", + "reward_terms": "Per-environment weighted rates from public get_active_iterable_terms(); before control-dt scaling. reward is the actual returned scalar. Do not compare rates directly with reward or across changed weights.", + "action_near_bound_definition": "abs(normalized policy action) >= 0.95; NOT torque saturation", + "control_contract": { + "action_type": "JointPositionAction", + "joint_observation_order": [ + "FL_hip_joint", + "FL_thigh_joint", + "FL_calf_joint", + "FR_hip_joint", + "FR_thigh_joint", + "FR_calf_joint", + "RL_hip_joint", + "RL_thigh_joint", + "RL_calf_joint", + "RR_hip_joint", + "RR_thigh_joint", + "RR_calf_joint" + ], + "action_target_joint_order": [ + "FL_hip_joint", + "FL_thigh_joint", + "FL_calf_joint", + "FR_hip_joint", + "FR_thigh_joint", + "FR_calf_joint", + "RL_hip_joint", + "RL_thigh_joint", + "RL_calf_joint", + "RR_hip_joint", + "RR_thigh_joint", + "RR_calf_joint" + ], + "action_target_joint_indices": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11 + ], + "scale": 0.25, + "offset_rad": [ + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ], + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ], + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ], + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ], + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ], + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ], + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ], + [ + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 0.800000011920929, + -1.5, + 0.0, + 1.0, + -1.5, + 0.0, + 1.0, + -1.5 + ] + ], + "clip_config": null, + "note": "Resolved position-action affine mapping: target = offset + scale * action, before configured clipping and encoder-bias compensation. This is configuration evidence, not a measurement of actuator torque or proof of correct physical response." + }, + "runtime_source_sha256": { + "scripts/evaluate_rma.py": "6a0ab5072dcc52a434be29a0b1ec212cdcff22edee9651d78f61b3a300b004ff", + "rsl_rl/unilab_rma/evaluation_trace.py": "54ddd1806811fd63c0c6df58010a484a4da4a4b7ecc176014186f6c7bcf42209", + "rsl_rl/unilab_rma/env_adapter.py": "3b29df75056b3303162020b64b037b100f45597de072339909cd3f72e1f831fc", + "src/unilab/envs/mdp/actions/actions.py": "631a2c5853b9bfee35f92faebfb5a6e8d9abd5308f701b86ca72ac97af108daa", + "src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml": "1f91628cc0201affda56c1b7363469224100ad5d59c2421826d44cc58d399dce", + "src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml": "57d4fa8552601cdc151f5dc3e9dee48dbb0281e25f6e4138225414ec9450ddc9", + "src/unilab/conf/ppo/task/go2_joystick_flat/rma.yaml": "f8fa2851e1ee6e0d4a078d5e027367244e245c5ec990d4d56b8da731143da3d1", + 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"mean_weighted_reward_rates_before_dt": { + "tracking_lin_vel": 0.7773760459618643, + "tracking_ang_vel": 0.19211307309247833, + "lin_vel_z": -0.005984629674967392, + "ang_vel_xy": -0.015748580673659234, + "base_height": -0.023337925799274715, + "action_rate": -0.003540008571808016, + "similar_to_default": -0.22660776428505777, + "contact": 0.17592749336827546, + "swing_feet_z": 0.059792615123093126 + } + } +} diff --git a/results/rma/diagnostics/EXP-002/seed123/transitions.csv.gz b/results/rma/diagnostics/EXP-002/seed123/transitions.csv.gz new file mode 100644 index 000000000..c05299a36 Binary files /dev/null and b/results/rma/diagnostics/EXP-002/seed123/transitions.csv.gz differ diff --git a/results/rma/evaluation_summary.csv b/results/rma/evaluation_summary.csv new file mode 100644 index 000000000..94c3ebbc7 --- /dev/null +++ b/results/rma/evaluation_summary.csv @@ -0,0 +1,7 @@ +model,seed,target_vx_m_s,mean_vx_m_s,vx_rmse_m_s,planar_rmse_m_s,terminations,total_transitions +baseline,123,0.5,-0.006058229419679051,0.5109570487642697,0.5146384602254557,0,8000 +baseline,456,0.5,-0.011666606367917325,0.5174359250308406,0.5223854549035621,1,8000 +baseline,789,0.5,-0.004921973013823731,0.5110722221933418,0.5156226711943084,1,8000 +forward_v1,123,0.5,0.015685936529645686,0.4897715884761797,0.49315400810945587,2,8000 +forward_v1,456,0.5,0.013729549131713991,0.49066846115678453,0.49315459737476025,0,8000 +forward_v1,789,0.5,0.012253768285125432,0.49195942077463545,0.49402653605180963,0,8000 diff --git a/results/rma/figures/forward_v1_training.png b/results/rma/figures/forward_v1_training.png new file mode 100644 index 000000000..5c444d76b Binary files /dev/null and b/results/rma/figures/forward_v1_training.png differ diff --git a/results/rma/figures/velocity_comparison.png b/results/rma/figures/velocity_comparison.png new file mode 100644 index 000000000..802b5113a Binary files /dev/null and b/results/rma/figures/velocity_comparison.png differ diff --git a/results/rma/forward_v1/checkpoint_metadata.json b/results/rma/forward_v1/checkpoint_metadata.json new file mode 100644 index 000000000..6bdfb3205 --- /dev/null +++ b/results/rma/forward_v1/checkpoint_metadata.json @@ -0,0 +1,20 @@ +{ + "checkpoint": "teacher_forward_v1.pt", + "sha256": "5454f899a358932d7208aaf52e82937e1db51f3f71609f1a79204bcc2f6a8fb9", + "format": "unilab-rma-v1", + "phase": "teacher", + "completed_iterations": 3000, + "model_config": { + "obs_dim": 49, + "critic_dim": 52, + "privileged_dim": 2, + "history_length": 50, + "latent_dim": 8, + "action_dim": 12, + "hidden": [ + 256, + 128 + ] + }, + "weights_included": false +} diff --git a/results/rma/forward_v1/config_snapshot/base.yaml b/results/rma/forward_v1/config_snapshot/base.yaml new file mode 100644 index 000000000..4886ea6ee --- /dev/null +++ b/results/rma/forward_v1/config_snapshot/base.yaml @@ -0,0 +1,239 @@ +# @package _global_ +# Canonical Go2 flat Manager-Based task declaration. Backend owner leaves inherit +# this file and only override backend/algo tuning or explicitly disabled terms. +env: + scene: + model_file: src/unilab/assets/robots/go2/scene_flat.xml + default_keyframe_name: home + entities: + robot: + root_body_name: base + body_names: [FL_calf] + joint_names: + - FL_hip_joint + - FL_thigh_joint + - FL_calf_joint + - FR_hip_joint + - FR_thigh_joint + - FR_calf_joint + - RL_hip_joint + - RL_thigh_joint + - RL_calf_joint + - RR_hip_joint + - RR_thigh_joint + - RR_calf_joint + actuator_names: + - FR_hip + - FR_thigh + - FR_calf + - FL_hip + - FL_thigh + - FL_calf + - RR_hip + - RR_thigh + - RR_calf + - RL_hip + - RL_thigh + - RL_calf + sim_dt: 0.01 + ctrl_dt: 0.02 + max_episode_seconds: 20.0 + observations: + policy: + terms: + base_ang_vel: + func: unilab.envs.mdp.builtin_sensor + params: + sensor_name: gyro + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + projected_gravity: + func: unilab.envs.mdp.projected_gravity_from_sensor + params: + sensor_name: upvector + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + joint_pos: + func: unilab.envs.mdp.joint_pos_rel + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + joint_vel: + func: unilab.envs.mdp.joint_vel_rel + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + actions: + func: unilab.envs.mdp.last_action + command: + func: unilab.envs.mdp.generated_commands + params: + command_name: twist + gait_phase: + func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase + params: + frequency: 2.0 + critic: + terms: + base_ang_vel: + func: unilab.envs.mdp.builtin_sensor + params: + sensor_name: gyro + projected_gravity: + func: unilab.envs.mdp.projected_gravity_from_sensor + params: + sensor_name: upvector + joint_pos: + func: unilab.envs.mdp.joint_pos_rel + joint_vel: + func: unilab.envs.mdp.joint_vel_rel + actions: + func: unilab.envs.mdp.last_action + command: + func: unilab.envs.mdp.generated_commands + params: + command_name: twist + gait_phase: + func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase + params: + frequency: 2.0 + base_lin_vel: + func: unilab.envs.mdp.builtin_sensor + params: + sensor_name: local_linvel + actions: + joint_pos: + _target_: unilab.envs.mdp.JointPositionActionCfg + entity_name: robot + actuator_names: [".*"] + scale: 0.25 + use_default_offset: true + commands: + twist: + _target_: unilab.envs.mdp.UniformVelocityCommandCfg + entity_name: robot + resampling_time_range: [20.0, 20.0] + heading_command: false + heading_control_stiffness: 0.5 + rel_standing_envs: 0.0 + rel_heading_envs: 0.0 + rel_world_envs: 0.0 + rel_forward_envs: 0.0 + init_velocity_prob: 0.0 + ranges: + lin_vel_x: [-0.6, 1.0] + lin_vel_y: [-0.4, 0.4] + ang_vel_z: [-0.8, 0.8] + events: + reset_scene_to_default: + func: unilab.envs.mdp.reset_scene_to_default + mode: reset + reset_root_state_uniform: + func: unilab.envs.mdp.reset_root_state_uniform + mode: reset + params: + pose_range: + x: [-0.5, 0.5] + y: [-0.5, 0.5] + z: [0.0, 0.0] + roll: [0.0, 0.0] + pitch: [0.0, 0.0] + yaw: [-3.141592653589793, 3.141592653589793] + velocity_range: + x: [-0.5, 0.5] + y: [-0.5, 0.5] + z: [-0.5, 0.5] + roll: [-0.5, 0.5] + pitch: [-0.5, 0.5] + yaw: [-0.5, 0.5] + randomize_leg_mass: + func: unilab.envs.mdp.randomize_rigid_body_mass + mode: reset + params: + asset_cfg: + _target_: unilab.managers.SceneEntityCfg + name: robot + body_names: [FL_calf] + operation: add + distribution: uniform + mass_distribution_params: [10.0, 15.0] + recompute_inertia: false + pd_gains: + func: unilab.envs.mdp.pd_gains + mode: reset + params: + kp_range: [31.5, 38.5] + kd_range: [0.45, 0.55] + operation: abs + terminations: + time_out: + func: unilab.envs.mdp.time_out + time_out: true + bad_orientation: + func: unilab.envs.mdp.bad_orientation + params: + limit_angle: 1.0471975511965976 + policy_observation_group: policy + critic_observation_group: critic + +reward: + tracking_lin_vel: + func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp + weight: 1.0 + params: + std: 0.5 + command_name: twist + tracking_ang_vel: + func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp + weight: 0.2 + params: + std: 0.5 + command_name: twist + lin_vel_z: + func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2 + weight: -5.0 + ang_vel_xy: + func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2 + weight: -0.1 + base_height: + func: unilab.tasks.locomotion.common.manager_terms.base_height_l2 + weight: -100.0 + params: + target_height: 0.3 + action_rate: + func: unilab.envs.mdp.action_rate_l2 + weight: -0.005 + similar_to_default: + func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1 + weight: -0.1 + contact: + func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact + weight: 0.24 + params: + frequency: 2.0 + sensor_names: + - FL_foot_contact + - FR_foot_contact + - RL_foot_contact + - RR_foot_contact + contact_threshold: 0.1 + stance_threshold: 0.6 + swing_feet_z: + func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height + weight: 4.0 + params: + frequency: 2.0 + sensor_names: [FL_pos, FR_pos, RL_pos, RR_pos] + target_height: 0.1 + kernel: 0.01 + swing_start: 0.6 diff --git a/results/rma/forward_v1/config_snapshot/composed_unresolved.yaml b/results/rma/forward_v1/config_snapshot/composed_unresolved.yaml new file mode 100644 index 000000000..47b6f3486 --- /dev/null +++ b/results/rma/forward_v1/config_snapshot/composed_unresolved.yaml @@ -0,0 +1,438 @@ +algo: + algo: ppo + algo_log_name: rsl_rl_ppo + seed: 1 + num_envs: 1024 + num_steps_per_env: 24 + max_iterations: 151 + save_interval: 100 + obs_groups: + actor: + - policy + critic: + - critic + experiment_name: test + run_name: '' + resume: false + load_run: '-1' + checkpoint: -1 + resume_path: null + actor: + class_name: rsl_rl.models.MLPModel + hidden_dims: + - 512 + - 256 + - 128 + activation: elu + obs_normalization: true + distribution_cfg: + class_name: rsl_rl.modules.distribution.GaussianDistribution + init_std: 0.5 + std_type: scalar + critic: + class_name: rsl_rl.models.MLPModel + hidden_dims: + - 512 + - 256 + - 128 + activation: elu + obs_normalization: true + algorithm: + class_name: rsl_rl.algorithms:PPO + value_loss_coef: 1.0 + use_clipped_value_loss: true + clip_param: 0.2 + entropy_coef: 0.001 + num_learning_epochs: 5 + num_mini_batches: 4 + learning_rate: 0.0003 + schedule: adaptive + gamma: 0.99 + lam: 0.95 + desired_kl: 0.01 + max_grad_norm: 1.0 +training: + task_name: Go2JoystickFlat + dp_collector_cpu_ids: null + device: null + logger: tensorboard + log_interval: 1 + wandb_project: unilab + wandb_entity: null + wandb_group: null + wandb_job_type: null + wandb_name: null + wandb_tags: [] + wandb_notes: null + wandb_mode: null + sim_backend: mujoco + nan_guard: + enabled: true + buffer_size: 100 + max_envs_to_dump: 5 + output_dir: null + play_only: false + no_play: false + sim2sim_strict: true + play_render_mode: auto + play_env_num: 16 + render_spacing: 1.0 + play_steps: 200 + cam_distance: 6.0 + cam_elevation: -20.0 + cam_azimuth: 90.0 + cam_lookat: null + cam_tracking: false + cam_tracking_env_idx: 0 + cam_tracking_extra_envs: 2 + log_root: null + num_timesteps: null + log_dir: null +interactive: + action_mode: zero + policy_obs_mode: auto + show_target_bodies: false + show_reward_debug: false + target_show_axes: false + target_body_names: '' + target_max_bodies: 0 + target_marker_radius: 0.02 + target_axis_length: 0.08 + target_marker_alpha: 0.75 + reward_debug_show_velocity: false + reward_debug_lin_vel_scale: 0.08 + reward_debug_ang_vel_scale: 0.05 + reward_debug_show_connectors: false + reward_debug_show_global_anchor: false + camera_follow_body: true + camera_focus_body_name: '' + camera_height_offset: 0.15 + camera_distance: null + camera_elevation: null + camera_azimuth: null + use_env_visual_model: true + speed: 1.0 + start_paused: false + keyboard: false + keyboard_step_lin: 0.1 + keyboard_step_ang: 0.2 +viser: + port: 8080 + env_idx: 0 + display_mode: all + max_envs: 16 +env: + scene: + model_file: src/unilab/assets/robots/go2/scene_flat.xml + default_keyframe_name: home + entities: + robot: + root_body_name: base + body_names: + - FL_calf + - base + joint_names: + - FL_hip_joint + - FL_thigh_joint + - FL_calf_joint + - FR_hip_joint + - FR_thigh_joint + - FR_calf_joint + - RL_hip_joint + - RL_thigh_joint + - RL_calf_joint + - RR_hip_joint + - RR_thigh_joint + - RR_calf_joint + actuator_names: + - FR_hip + - FR_thigh + - FR_calf + - FL_hip + - FL_thigh + - FL_calf + - RR_hip + - RR_thigh + - RR_calf + - RL_hip + - RL_thigh + - RL_calf + geom_names: [] + sim_dt: 0.01 + ctrl_dt: 0.02 + max_episode_seconds: 20.0 + observations: + policy: + terms: + base_ang_vel: + func: unilab.envs.mdp.builtin_sensor + params: + sensor_name: gyro + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + projected_gravity: + func: unilab.envs.mdp.projected_gravity_from_sensor + params: + sensor_name: upvector + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + joint_pos: + func: unilab.envs.mdp.joint_pos_rel + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + joint_vel: + func: unilab.envs.mdp.joint_vel_rel + noise: + _target_: unilab.managers._noise.UniformNoiseCfg + n_min: -0.2 + n_max: 0.2 + operation: add + actions: + func: unilab.envs.mdp.last_action + command: + func: unilab.envs.mdp.generated_commands + params: + command_name: twist + gait_phase: + func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase + params: + frequency: 2.0 + critic: + terms: + base_ang_vel: + func: unilab.envs.mdp.builtin_sensor + params: + sensor_name: gyro + projected_gravity: + func: unilab.envs.mdp.projected_gravity_from_sensor + params: + sensor_name: upvector + joint_pos: + func: unilab.envs.mdp.joint_pos_rel + joint_vel: + func: unilab.envs.mdp.joint_vel_rel + actions: + func: unilab.envs.mdp.last_action + command: + func: unilab.envs.mdp.generated_commands + params: + command_name: twist + gait_phase: + func: unilab.tasks.locomotion.common.manager_terms.quadruped_gait_phase + params: + frequency: 2.0 + base_lin_vel: + func: unilab.envs.mdp.builtin_sensor + params: + sensor_name: local_linvel + privileged: + terms: + parameters: + func: unilab.envs.mdp.rma.privileged_parameters + actions: + joint_pos: + _target_: unilab.envs.mdp.JointPositionActionCfg + entity_name: robot + actuator_names: + - .* + scale: 0.25 + use_default_offset: true + commands: + twist: + _target_: unilab.envs.mdp.UniformVelocityCommandCfg + entity_name: robot + resampling_time_range: + - 20.0 + - 20.0 + heading_command: false + heading_control_stiffness: 0.5 + rel_standing_envs: 0.0 + rel_heading_envs: 0.0 + rel_world_envs: 0.0 + rel_forward_envs: 0.0 + init_velocity_prob: 0.0 + ranges: + lin_vel_x: + - 0.3 + - 0.6 + lin_vel_y: + - 0.0 + - 0.0 + ang_vel_z: + - 0.0 + - 0.0 + events: + reset_scene_to_default: + func: unilab.envs.mdp.reset_scene_to_default + mode: reset + reset_root_state_uniform: + func: unilab.envs.mdp.reset_root_state_uniform + mode: reset + params: + pose_range: + x: + - -0.5 + - 0.5 + 'y': + - -0.5 + - 0.5 + z: + - 0.0 + - 0.0 + roll: + - 0.0 + - 0.0 + pitch: + - 0.0 + - 0.0 + yaw: + - -3.141592653589793 + - 3.141592653589793 + velocity_range: + x: + - -0.5 + - 0.5 + 'y': + - -0.5 + - 0.5 + z: + - -0.5 + - 0.5 + roll: + - -0.5 + - 0.5 + pitch: + - -0.5 + - 0.5 + yaw: + - -0.5 + - 0.5 + randomize_leg_mass: null + pd_gains: + func: unilab.envs.mdp.pd_gains + mode: reset + params: + kp_range: + - 31.5 + - 38.5 + kd_range: + - 0.45 + - 0.55 + operation: abs + rma_base_load: + func: unilab.envs.mdp.rma.RMABaseMass + mode: reset + params: + asset_cfg: + _target_: unilab.managers.SceneEntityCfg + name: robot + body_names: + - base + operation: add + distribution: uniform + mass_distribution_params: + - 0.0 + - 5.0 + recompute_inertia: false + rma_friction: + func: unilab.envs.mdp.rma.RMAFriction + mode: reset + params: + asset_cfg: + _target_: unilab.managers.SceneEntityCfg + name: robot + geom_names: + - .* + ranges: + - 0.4 + - 1.25 + axes: + - 0 + shared_random: true + operation: abs + terminations: + time_out: + func: unilab.envs.mdp.time_out + time_out: true + bad_orientation: + func: unilab.envs.mdp.bad_orientation + params: + limit_angle: 1.0471975511965976 + policy_observation_group: policy + critic_observation_group: critic + auto_reset: false +reward: + tracking_lin_vel: + func: unilab.tasks.locomotion.common.manager_terms.track_lin_vel_xy_exp + weight: 2.0 + params: + std: 0.5 + command_name: twist + tracking_ang_vel: + func: unilab.tasks.locomotion.common.manager_terms.track_ang_vel_z_exp + weight: 0.2 + params: + std: 0.5 + command_name: twist + lin_vel_z: + func: unilab.tasks.locomotion.common.manager_terms.lin_vel_z_l2 + weight: -5.0 + ang_vel_xy: + func: unilab.tasks.locomotion.common.manager_terms.ang_vel_xy_l2 + weight: -0.1 + base_height: + func: unilab.tasks.locomotion.common.manager_terms.base_height_l2 + weight: -100.0 + params: + target_height: 0.3 + action_rate: + func: unilab.envs.mdp.action_rate_l2 + weight: -0.005 + similar_to_default: + func: unilab.tasks.locomotion.common.manager_terms.joint_deviation_l1 + weight: -0.1 + contact: + func: unilab.tasks.locomotion.common.manager_terms.feet_phase_contact + weight: 0.24 + params: + frequency: 2.0 + sensor_names: + - FL_foot_contact + - FR_foot_contact + - RL_foot_contact + - RR_foot_contact + contact_threshold: 0.1 + stance_threshold: 0.6 + swing_feet_z: + func: unilab.tasks.locomotion.common.manager_terms.feet_phase_swing_height + weight: 0.25 + params: + frequency: 2.0 + sensor_names: + - FL_pos + - FR_pos + - RL_pos + - RR_pos + target_height: 0.1 + kernel: 0.01 + swing_start: 0.6 +play_profile: + enabled: true + env: + render_spacing: 2.0 +rma: + obs_dim: 49 + critic_dim: 52 + privileged_dim: 2 + history_length: 50 + latent_dim: 8 + action_dim: 12 diff --git a/results/rma/forward_v1/config_snapshot/mujoco.yaml b/results/rma/forward_v1/config_snapshot/mujoco.yaml new file mode 100644 index 000000000..eb743cfa1 --- /dev/null +++ b/results/rma/forward_v1/config_snapshot/mujoco.yaml @@ -0,0 +1,29 @@ +# @package _global_ +defaults: + - /task/go2_joystick_flat/base + - _self_ + +training: + task_name: Go2JoystickFlat + sim_backend: mujoco +algo: + num_envs: 1024 + max_iterations: 151 + obs_groups: + actor: + - actor + critic: + - critic + actor: + obs_normalization: true + distribution_cfg: + init_std: 0.5 + critic: + obs_normalization: true + algorithm: + learning_rate: 3.0e-4 + entropy_coef: 1.0e-3 +play_profile: + enabled: true + env: + render_spacing: 2.0 diff --git a/results/rma/forward_v1/config_snapshot/rma.yaml b/results/rma/forward_v1/config_snapshot/rma.yaml new file mode 100644 index 000000000..24bc7c893 --- /dev/null +++ b/results/rma/forward_v1/config_snapshot/rma.yaml @@ -0,0 +1,60 @@ +# @package _global_ +defaults: + - /task/go2_joystick_flat/mujoco + - _self_ + +# Existing non-RMA configs remain unchanged. train_rma.py composes this overlay. +env: + policy_observation_group: policy + critic_observation_group: critic + scene: + entities: + robot: + body_names: [FL_calf, base] + # train_rma.py fills exact named robot geoms from the restored XML assets. + geom_names: [] + observations: + privileged: + terms: + parameters: + func: unilab.envs.mdp.rma.privileged_parameters + events: + randomize_leg_mass: null + rma_base_load: + func: unilab.envs.mdp.rma.RMABaseMass + mode: reset + params: + asset_cfg: + _target_: unilab.managers.SceneEntityCfg + name: robot + body_names: [base] + operation: add + distribution: uniform + mass_distribution_params: [0.0, 5.0] + recompute_inertia: false + rma_friction: + func: unilab.envs.mdp.rma.RMAFriction + mode: reset + params: + asset_cfg: + _target_: unilab.managers.SceneEntityCfg + name: robot + geom_names: ['.*'] + ranges: [0.4, 1.25] + axes: [0] + shared_random: true + operation: abs + auto_reset: false + +algo: + obs_groups: + actor: [policy] + critic: [critic] + +rma: + obs_dim: 49 + critic_dim: 52 + privileged_dim: 2 + history_length: 50 + latent_dim: 8 + action_dim: 12 diff --git a/results/rma/forward_v1/config_snapshot/rma_forward.yaml b/results/rma/forward_v1/config_snapshot/rma_forward.yaml new file mode 100644 index 000000000..26f3e85d4 --- /dev/null +++ b/results/rma/forward_v1/config_snapshot/rma_forward.yaml @@ -0,0 +1,20 @@ +# @package _global_ +# Experimental forward-walking curriculum. Original rma task is unchanged. +# Same policy I/O, dynamics, action scale and randomization as the RMA baseline. +defaults: + - /task/go2_joystick_flat/rma + - _self_ + +env: + commands: + twist: + ranges: + lin_vel_x: [0.3, 0.6] + lin_vel_y: [0.0, 0.0] + ang_vel_z: [0.0, 0.0] + +reward: + tracking_lin_vel: + weight: 2.0 + swing_feet_z: + weight: 0.25 diff --git a/results/rma/forward_v1/run_configuration.json b/results/rma/forward_v1/run_configuration.json new file mode 100644 index 000000000..c68aba78f --- /dev/null +++ b/results/rma/forward_v1/run_configuration.json @@ -0,0 +1,35 @@ +{ + "arguments": { + "phase": "teacher", + "checkpoint": null, + "output": "runs/rma/teacher_forward_v1.pt", + "iterations": 3000, + "num_envs": 64, + "device": "cuda", + "task": "go2_joystick_flat/rma_forward", + "log_dir": null + }, + "model_config": { + "obs_dim": 49, + "critic_dim": 52, + "privileged_dim": 2, + "history_length": 50, + "latent_dim": 8, + "action_dim": 12, + "hidden": [ + 256, + 128 + ] + }, + "horizon": 24, + "seed": 42, + "torch_version": "2.14.0+cu130", + "requested_policy_device": "cuda", + "environment_device": "cpu", + "sim_backend": "mujoco", + "output": "runs/rma/teacher_forward_v1.pt", + "phase": "teacher", + "log_directory": "runs/rma/teacher_forward_v1_logs/20261022_180845_or3cyhd9", + "iteration_numbering": "one-based completed updates", + "publication_note": "Only absolute output/log paths were made relative; other fields unchanged." +} diff --git a/results/rma/forward_v1/seed123.json b/results/rma/forward_v1/seed123.json new file mode 100644 index 000000000..56212557d --- /dev/null +++ b/results/rma/forward_v1/seed123.json @@ -0,0 +1,62 @@ +{ + "policy": "teacher", + "seed": 123, + "completed_episodes": 9, + "mean_completed_episode_return": 11.619225793414646, + "mean_completed_episode_length_steps": 629.2222222222222, + "mean_step_reward": 0.01859980582818389, + "planar_velocity_rmse_m_per_s": 0.49315400810945587, + "unfinished_episodes": 8, + "mean_unfinished_episode_return": 5.528181076049805, + "student_latent_mse_vs_teacher": null, + "student_action_rmse_vs_teacher": null, + "terminations": 2, + "total_transitions": 8000, + "command_override": [ + 0.5, + 0.0, + 0.0 + ], + "task": "go2_joystick_flat/rma_forward", + "diagnostics": { + "velocity_axes": [ + "vx_m_per_s", + "vy_m_per_s", + "yaw_rate_rad_per_s" + ], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": [ + 0.5, + 0.0, + 0.0 + ], + "mean_command": [ + 0.5, + 0.0, + 0.0 + ], + "mean_measured_velocity": [ + 0.015685936529645686, + -0.007749902041240091, + 0.03442901849853064 + ], + "tracking_rmse_by_axis": [ + 0.4897715884761797, + 0.057659923685617856, + 0.130251946827097 + ], + "mean_absolute_yaw_rate_rad_per_s": 0.06008052522769907, + "weighted_reward_terms_before_dt": { + "tracking_lin_vel": 0.7773760445713996, + "tracking_ang_vel": 0.1921130729392171, + "lin_vel_z": -0.00598462969110642, + "ang_vel_xy": -0.015748580647712514, + "base_height": -0.02333792574959807, + "action_rate": -0.0035400085798974034, + "similar_to_default": -0.22660776471346616, + "contact": 0.17592749336361885, + "swing_feet_z": 0.059792615115642544 + }, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards" + } +} diff --git a/results/rma/forward_v1/seed456.json b/results/rma/forward_v1/seed456.json new file mode 100644 index 000000000..e2f5101d8 --- /dev/null +++ b/results/rma/forward_v1/seed456.json @@ -0,0 +1,62 @@ +{ + "policy": "teacher", + "seed": 456, + "completed_episodes": 8, + "mean_completed_episode_return": 10.60306566953659, + "mean_completed_episode_length_steps": 564.875, + "mean_step_reward": 0.01878373809804907, + "planar_velocity_rmse_m_per_s": 0.49315459737476025, + "unfinished_episodes": 8, + "mean_unfinished_episode_return": 8.180676460266113, + "student_latent_mse_vs_teacher": null, + "student_action_rmse_vs_teacher": null, + "terminations": 0, + "total_transitions": 8000, + "command_override": [ + 0.5, + 0.0, + 0.0 + ], + "task": "go2_joystick_flat/rma_forward", + "diagnostics": { + "velocity_axes": [ + "vx_m_per_s", + "vy_m_per_s", + "yaw_rate_rad_per_s" + ], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": [ + 0.5, + 0.0, + 0.0 + ], + "mean_command": [ + 0.5, + 0.0, + 0.0 + ], + "mean_measured_velocity": [ + 0.013729549131713991, + -0.005043574669553379, + 0.03246544610648264 + ], + "tracking_rmse_by_axis": [ + 0.49066846115678453, + 0.049456221048872574, + 0.10843041134148648 + ], + "mean_absolute_yaw_rate_rad_per_s": 0.05812633585727597, + "weighted_reward_terms_before_dt": { + "tracking_lin_vel": 0.7757972832918167, + "tracking_ang_vel": 0.19275773064047097, + "lin_vel_z": -0.003971626499741433, + "ang_vel_xy": -0.013042655649791414, + "base_height": -0.01996231985324994, + "action_rate": -0.0032092234268966424, + "similar_to_default": -0.2258007121682167, + "contact": 0.1770824935734272, + "swing_feet_z": 0.059535964109003546 + }, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards" + } +} diff --git a/results/rma/forward_v1/seed789.json b/results/rma/forward_v1/seed789.json new file mode 100644 index 000000000..38c5e78bb --- /dev/null +++ b/results/rma/forward_v1/seed789.json @@ -0,0 +1,62 @@ +{ + "policy": "teacher", + "seed": 789, + "completed_episodes": 8, + "mean_completed_episode_return": 11.019422739744186, + "mean_completed_episode_length_steps": 589.125, + "mean_step_reward": 0.01871223668009043, + "planar_velocity_rmse_m_per_s": 0.49402653605180963, + "unfinished_episodes": 8, + "mean_unfinished_episode_return": 7.692810535430908, + "student_latent_mse_vs_teacher": null, + "student_action_rmse_vs_teacher": null, + "terminations": 0, + "total_transitions": 8000, + "command_override": [ + 0.5, + 0.0, + 0.0 + ], + "task": "go2_joystick_flat/rma_forward", + "diagnostics": { + "velocity_axes": [ + "vx_m_per_s", + "vy_m_per_s", + "yaw_rate_rad_per_s" + ], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": [ + 0.5, + 0.0, + 0.0 + ], + "mean_command": [ + 0.5, + 0.0, + 0.0 + ], + "mean_measured_velocity": [ + 0.012253768285125432, + -0.004161755626676729, + 0.028055764691391885 + ], + "tracking_rmse_by_axis": [ + 0.49195942077463545, + 0.045145834817094645, + 0.0914933536134219 + ], + "mean_absolute_yaw_rate_rad_per_s": 0.04815842404206481, + "weighted_reward_terms_before_dt": { + "tracking_lin_vel": 0.7709526063203812, + "tracking_ang_vel": 0.19463158410787582, + "lin_vel_z": -0.0030863634385796105, + "ang_vel_xy": -0.013372091561352136, + "base_height": -0.01578838027827442, + "action_rate": -0.0033247425880035737, + "similar_to_default": -0.2310179681405425, + "contact": 0.17744999327510597, + "swing_feet_z": 0.059167224429547784 + }, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards" + } +} 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+注意：原 MuJoCo summary 的速度口径是动作执行前；跨引擎比较以 +`comparison.json` 的动作执行后口径为准。MuJoCo 原日志生成时模式名称尚未改为 v2， +但此次运行已使用 kd=0 和初始回合计数清零；保留原始元数据不回写。 + +## 离线复查 + +无需重新训练或启动仿真。将两份 gzip 分别解压为独立临时目录中的 +`mujoco/transitions.csv` 与 `genesis.csv`，再用 Python 3.10+ 和 Matplotlib 执行 +存档的 `compare_sim2sim_traces.py`，参数为： + +```text +--mujoco-trace <临时目录>/mujoco +--genesis-csv <临时目录>/genesis.csv +--output-dir <新的输出目录> +--command 0.5 0 0 +``` + +运行时副本与 GitHub fork 的源码版本不同；本次仅归档结果，不把运行目录整体覆盖到 fork。 +运行时没有 Git 历史，不能用当前 fork 的 commit 冒充该实验的源码版本。 +源文件与模型哈希记录于 provenance；该存档不含模型、全部运行源码、资产及完整依赖， +因此支持数据离线复算，不宣称仅克隆仓库即可重跑全部仿真。 + +`.pt`、`.onnx`、`.engine`、完整训练日志与资产留在本地；此目录只保存精选证据。 +下一步先记录降速时的接触与控制信号，区分实际力矩与代数估计，再选择一个对照变量。 diff --git a/results/rma/sim2sim/EXP-006/analysis_snapshot/NOMINAL_COMPARISON.md b/results/rma/sim2sim/EXP-006/analysis_snapshot/NOMINAL_COMPARISON.md new file mode 100644 index 000000000..da0048db6 --- /dev/null +++ b/results/rma/sim2sim/EXP-006/analysis_snapshot/NOMINAL_COMPARISON.md @@ -0,0 +1,48 @@ +# Go2 nominal cross-engine evaluation (v2) + +This is a frozen-policy comparison. Do not retrain or change rewards for this experiment. +Use the same `student_mapping_v1_500.pt` and its matching ONNX export. + +Changes are opt-in through `--sim2sim-nominal`; ordinary training is unchanged. +Both engines use home reset, zero initial velocities, zero observation noise, +friction 1.0, added payload 0, actuator kp 35 and kd 0, and the original passive +joint damping 0.5. Policy period is 0.02 s with two 0.01 s physics steps. +MuJoCo initial timeout counters are explicitly cleared, so the first episode is +1000 steps rather than a seed-dependent remaining training episode. + +Important sensor contract: the existing policy was trained on negative MJCF +framezaxis (the site z-axis expressed in world coordinates), despite the name +projected_gravity. Nominal Genesis now preserves that definition. Replacing it +with conventional body-frame projected gravity would change this policy input. +Do not alter the training definition for this frozen checkpoint. +Genesis velocity diagnostics include the IMU offset via omega cross r. + +Run a fresh comparison from the project root, keeping previous results: + +```bash +out="runs/rma/evaluations/nominal_$(date +%Y%m%d_%H%M%S)" +mkdir -p "$out" +set -o pipefail +uv run --no-sync python scripts/evaluate_rma.py --policy student --checkpoint runs/rma/student_mapping_v1_500.pt --task go2_joystick_flat/rma_forward --num-envs 1 --steps 1000 --seed 123 --command 0.5 0 0 --diagnostics --sim2sim-nominal --trace-dir "$out/mujoco" 2>&1 | tee "$out/mujoco.log" +scripts/run_genesis_go2.sh --steps 1000 --seed 123 --command 0.5 0 0 --sim2sim-nominal --output "$out/genesis.csv" 2>&1 | tee "$out/genesis.log" +uv run --no-sync python scripts/compare_sim2sim_traces.py --mujoco-trace "$out/mujoco" --genesis-csv "$out/genesis.csv" --output-dir "$out/plots" +``` + +Plot meanings: +- velocity_comparison.png: forward/lateral/yaw tracking against the command. +- action_near_bounds.png: fraction with normalized action magnitude >= 0.95; + this is not a torque or joint-limit saturation metric. +- genesis_stability.png: Genesis tilt and height over time. +- comparison.json: both metrics computed from post-step velocity to avoid mixing + the MuJoCo evaluator pre-step summary with Genesis post-step samples. + +The original seed123 validation lives in +`runs/rma/evaluations/sim2sim_nominal_v2_seed123`. Its MuJoCo raw log was +captured before the mode label was renamed to v2; actual kd=0 and initial +counter clearing were already active. Both traces end at step 1000, no falls. + +Limitations: matching settings does not make contact solvers or integrations +identical; no universal acceptance threshold is claimed. With home reset and +noise off, different seeds largely repeat the same condition. They are not a +robustness test over varied initial states or physical parameters. Validate +those separately after auditing the interfaces. diff --git a/results/rma/sim2sim/EXP-006/analysis_snapshot/compare_sim2sim_traces.py b/results/rma/sim2sim/EXP-006/analysis_snapshot/compare_sim2sim_traces.py new file mode 100644 index 000000000..7698e3ff9 --- /dev/null +++ b/results/rma/sim2sim/EXP-006/analysis_snapshot/compare_sim2sim_traces.py @@ -0,0 +1,107 @@ +"""Compare saved MuJoCo and Genesis runs; never loads or trains a policy.""" + +import argparse +import csv +import json +import math +from pathlib import Path + + +def load_rows(path): + with path.open(newline="", encoding="utf-8") as stream: + rows = list(csv.DictReader(stream)) + if not rows: + raise ValueError(f"Empty trace: {path}") + return rows + + +def series(rows, genesis=False): + names = ("vx_m_s", "vy_m_s", "yaw_rate_rad_s") if genesis else ( + "after_vx_m_s", "after_vy_m_s", "after_wz_rad_s" + ) + return [[float(row[name]) for row in rows] for name in names] + + +def metrics(rows, command, genesis=False): + values = series(rows, genesis) + rmse = [math.sqrt(sum((x - target) ** 2 for x in axis) / len(axis)) + for axis, target in zip(values, command, strict=True)] + falls = sum(row["reset_reason"] == "bad_orientation" for row in rows) if genesis else sum( + int(row["terminated"]) for row in rows + ) + return dict( + samples=len(rows), mean_velocity=[sum(axis) / len(axis) for axis in values], + rmse_by_axis=rmse, planar_rmse_m_s=math.hypot(rmse[0], rmse[1]), + fall_or_termination_count=falls, + near_bound_fraction=[sum(abs(float(row[f"action_{i}"])) >= 0.95 for row in rows) / len(rows) + for i in range(12)], + ) + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--mujoco-trace", type=Path, required=True) + p.add_argument("--genesis-csv", type=Path, required=True) + p.add_argument("--output-dir", type=Path, required=True) + p.add_argument("--command", type=float, nargs=3, default=[0.5, 0.0, 0.0]) + args = p.parse_args() + candidates = list(args.mujoco_trace.rglob("transitions.csv")) + if len(candidates) != 1: + p.error(f"Expected one MuJoCo trace, got {len(candidates)}; select its exact directory") + mj, gs = load_rows(candidates[0]), load_rows(args.genesis_csv) + if {row["env_id"] for row in mj} != {"0"}: + p.error("Use a single-environment MuJoCo trace") + args.output_dir.mkdir(parents=True, exist_ok=True) + summary = dict( + command=args.command, mujoco=metrics(mj, args.command), genesis=metrics(gs, args.command, True), + measurement="Both use post-action IMU-site velocity; yaw rate is rad/s", + limitation="Nominal interface-aligned comparison; contact solvers remain different. No universal pass threshold.", + sources=dict(mujoco=str(candidates[0].resolve()), genesis=str(args.genesis_csv.resolve())), + ) + (args.output_dir / "comparison.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + t_mj = [float(row["next_time_s"]) for row in mj] + t_gs = [float(row["sim_time_s"]) for row in gs] + fig, axes = plt.subplots(3, 1, figsize=(12, 8), sharex=True, constrained_layout=True) + for i, (ax, label) in enumerate(zip(axes, ("vx (m/s)", "vy (m/s)", "yaw rate (rad/s)"), strict=True)): + ax.plot(t_mj, series(mj)[i], label="MuJoCo", linewidth=1, alpha=0.8) + ax.plot(t_gs, series(gs, True)[i], label="Genesis", linewidth=1, alpha=0.8) + ax.axhline(args.command[i], linestyle="--", color="black", label="Command") + ax.set_ylabel(label) + ax.grid(alpha=0.2) + axes[0].legend(ncol=3) + axes[0].set_title("Frozen student, nominal evaluation; post-step IMU measurements") + axes[-1].set_xlabel("Simulation time (s)") + fig.savefig(args.output_dir / "velocity_comparison.png", dpi=160) + plt.close(fig) + + fig, ax = plt.subplots(figsize=(12, 4), constrained_layout=True) + x = list(range(12)) + ax.bar([i - 0.2 for i in x], summary["mujoco"]["near_bound_fraction"], width=0.4, label="MuJoCo") + ax.bar([i + 0.2 for i in x], summary["genesis"]["near_bound_fraction"], width=0.4, label="Genesis") + names = [f"{leg}_{joint}" for leg in ("FL", "FR", "RL", "RR") for joint in ("hip", "thigh", "calf")] + ax.set_xticks(x, names, rotation=35) + ax.set_ylabel("Fraction with |action| >= 0.95") + ax.set_title("Normalized action proximity to bounds; not joint-limit or torque saturation") + ax.legend() + fig.savefig(args.output_dir / "action_near_bounds.png", dpi=160) + plt.close(fig) + + fig, axes = plt.subplots(2, 1, figsize=(12, 5), sharex=True, constrained_layout=True) + axes[0].plot(t_gs, [float(row["tilt_deg"]) for row in gs]) + axes[0].set_ylabel("Tilt (deg)") + axes[1].plot(t_gs, [float(row["base_z_m"]) for row in gs]) + axes[1].set_ylabel("Base height (m)") + axes[1].set_xlabel("Simulation time (s)") + axes[0].set_title("Genesis stability trace") + fig.savefig(args.output_dir / "genesis_stability.png", dpi=160) + plt.close(fig) + print(json.dumps(summary, indent=2)) + print(f"Saved three plots in {args.output_dir.resolve()}") + + +if __name__ == "__main__": + main() diff --git a/results/rma/sim2sim/EXP-006/comparison.json b/results/rma/sim2sim/EXP-006/comparison.json new file mode 100644 index 000000000..db0fe36b5 --- /dev/null +++ b/results/rma/sim2sim/EXP-006/comparison.json @@ -0,0 +1,71 @@ +{ + "command": [ + 0.5, + 0.0, + 0.0 + ], + "mujoco": { + "samples": 1000, + "mean_velocity": [ + 0.5520572142675519, + 0.02310534722799457, + -0.08643446249634144 + ], + "rmse_by_axis": [ + 0.10183338166805582, + 0.04847269512295537, + 0.16515393788608937 + ], + "planar_rmse_m_s": 0.11278138053080795, + "fall_or_termination_count": 0, + "near_bound_fraction": [ + 0.016, + 0.124, + 0.118, + 0.003, + 0.013, + 0.258, + 0.03, + 0.014, + 0.044, + 0.122, + 0.155, + 0.417 + ] + }, + "genesis": { + "samples": 1000, + "mean_velocity": [ + 0.4867710067454318, + 0.027043490791460498, + 0.03830585028952919 + ], + "rmse_by_axis": [ + 0.16554040927260974, + 0.05645601348660588, + 0.15112661729348884 + ], + "planar_rmse_m_s": 0.17490256876599314, + "fall_or_termination_count": 0, + "near_bound_fraction": [ + 0.002, + 0.096, + 0.116, + 0.0, + 0.01, + 0.261, + 0.011, + 0.005, + 0.032, + 0.125, + 0.12, + 0.449 + ] + }, + "measurement": "Both use post-action IMU-site velocity; yaw rate is rad/s", + "limitation": "Nominal interface-aligned comparison; contact solvers remain different. No universal pass threshold.", + "sources": { + "mujoco": "/home/abc/my_unilab/runs/rma/evaluations/sim2sim_nominal_v2_seed123/mujoco/eval_3wq40pkr/transitions.csv", + "genesis": "/home/abc/my_unilab/runs/rma/evaluations/sim2sim_nominal_v2_seed123/genesis.csv" + } +} \ No newline at end of file diff --git a/results/rma/sim2sim/EXP-006/mujoco/summary.json b/results/rma/sim2sim/EXP-006/mujoco/summary.json new file mode 100644 index 000000000..73b60c83c --- /dev/null +++ b/results/rma/sim2sim/EXP-006/mujoco/summary.json @@ -0,0 +1,173 @@ +{ + "policy": "student", + "seed": 123, + "completed_episodes": 1, + "mean_completed_episode_return": 41.99813461303711, + "mean_completed_episode_length_steps": 1000.0, + "mean_step_reward": 0.04199817029200494, + "planar_velocity_rmse_m_per_s": 0.11375569429588679, + "unfinished_episodes": 0, + "mean_unfinished_episode_return": null, + "student_latent_mse_vs_teacher": 0.009088981525565032, + "student_action_rmse_vs_teacher": 0.01922868809264916, + "terminations": 0, + "total_transitions": 1000, + "command_override": [ + 0.5, + 0.0, + 0.0 + ], + "evaluation_mode": "sim2sim_nominal_home_zero_noise", + "task": "go2_joystick_flat/rma_forward", + "diagnostics": { + "velocity_axes": [ + "vx_m_per_s", + "vy_m_per_s", + "yaw_rate_rad_per_s" + ], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": [ + 0.5, + 0.0, + 0.0 + ], + "mean_command": [ + 0.5, + 0.0, + 0.0 + ], + "mean_measured_velocity": [ + 0.55138948494941, + 0.023139239531699787, + -0.0866801185098302 + ], + "tracking_rmse_by_axis": [ + 0.1029169786662413, + 0.04846084485677623, + 0.16497113784634895 + ], + "mean_absolute_yaw_rate_rad_per_s": 0.13216670325931046, + "weighted_reward_terms_before_dt": { + "tracking_lin_vel": 1.9098223032951356, + "tracking_ang_vel": 0.1807469550538808, + "lin_vel_z": -0.011709675100798652, + "ang_vel_xy": -0.03375083813566289, + "base_height": -0.03783127726802195, + "action_rate": -0.006751242972022738, + "similar_to_default": -0.15488274090364576, + "contact": 0.18557999498769642, + "swing_feet_z": 0.06868506468459964 + }, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards" + }, + "trace": { + "directory": "/home/abc/my_unilab/runs/rma/evaluations/sim2sim_nominal_v2_seed123/mujoco/eval_3wq40pkr", + "full": { + "transitions": 1000, + "mean_measured_velocity": [ + 0.55138948494941, + 0.023139239531699787, + -0.0866801185098302 + ], + "tracking_rmse_by_axis": [ + 0.1029169786662413, + 0.04846084485677623, + 0.16497113784634895 + ] + }, + "after_startup_window": { + "transitions": 900, + "mean_measured_velocity": [ + 0.556280231806967, + 0.021171637030127182, + -0.08470533217423104 + ], + "tracking_rmse_by_axis": [ + 0.09695333675688375, + 0.04586780016924982, + 0.16490015415929535 + ] + }, + "action_target_joint_order": [ + "FL_hip_joint", + "FL_thigh_joint", + "FL_calf_joint", + "FR_hip_joint", + "FR_thigh_joint", + "FR_calf_joint", + "RL_hip_joint", + "RL_thigh_joint", + "RL_calf_joint", + "RR_hip_joint", + "RR_thigh_joint", + "RR_calf_joint" + ], + "normalized_action_min": [ + -0.9902253746986389, + -0.9999876022338867, + -0.9998041391372681, + -0.8324918746948242, + -0.9374557137489319, + -0.9901061058044434, + -0.9884268641471863, + -0.9644988179206848, + -0.7830153107643127, + -0.9961377382278442, + -0.9996283054351807, + -0.9996256828308105 + ], + "normalized_action_max": [ + 0.9829237461090088, + 0.986842930316925, + 0.9712881445884705, + 0.9611890316009521, + 0.9720931053161621, + 0.9999984502792358, + 0.904410183429718, + 0.9691876173019409, + 0.9849426746368408, + 0.49857503175735474, + 0.9942507743835449, + 0.9998595118522644 + ], + "normalized_action_rms": [ + 0.7496004436854341, + 0.620985925763891, + 0.698776103261185, + 0.5772129133694048, + 0.5022383096920203, + 0.6929835285429926, + 0.5573535884985332, + 0.5070338913197341, + 0.6028693393574991, + 0.655578725800352, + 0.6217977881914584, + 0.867340289949644 + ], + "normalized_action_near_bound_fraction": [ + 0.016, + 0.124, + 0.118, + 0.003, + 0.013, + 0.258, + 0.03, + 0.014, + 0.044, + 0.122, + 0.155, + 0.417 + ], + "mean_weighted_reward_rates_before_dt": { + "tracking_lin_vel": 1.9098223032951356, + "tracking_ang_vel": 0.1807469550538808, + "lin_vel_z": -0.011709675100798652, + "ang_vel_xy": -0.03375083813566289, + "base_height": -0.03783127726802195, + "action_rate": -0.006751242972022738, + "similar_to_default": -0.15488274090364576, + "contact": 0.18557999498769642, + "swing_feet_z": 0.06868506468459964 + } + } +} diff --git a/results/rma/sim2sim/EXP-006/plots/action_near_bounds.png b/results/rma/sim2sim/EXP-006/plots/action_near_bounds.png new file mode 100644 index 000000000..264e8dbb6 Binary files /dev/null and b/results/rma/sim2sim/EXP-006/plots/action_near_bounds.png differ diff --git a/results/rma/sim2sim/EXP-006/plots/genesis_stability.png b/results/rma/sim2sim/EXP-006/plots/genesis_stability.png new file mode 100644 index 000000000..c6db566c7 Binary files /dev/null and b/results/rma/sim2sim/EXP-006/plots/genesis_stability.png differ diff --git a/results/rma/sim2sim/EXP-006/plots/velocity_comparison.png b/results/rma/sim2sim/EXP-006/plots/velocity_comparison.png new file mode 100644 index 000000000..55bc60b47 Binary files /dev/null and b/results/rma/sim2sim/EXP-006/plots/velocity_comparison.png differ diff --git a/results/rma/sim2sim/EXP-006/provenance.json b/results/rma/sim2sim/EXP-006/provenance.json new file mode 100644 index 000000000..f2e841e0f --- /dev/null +++ b/results/rma/sim2sim/EXP-006/provenance.json @@ -0,0 +1,78 @@ +{ + "date": "2026-10-24", + "experiment": "EXP-006", + "source_runtime": "WSL UniLab source-only runtime; 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deliberately does not shadow the rsl_rl package.""" + +from .actor_critic_rma import Actor, ActorCriticRMA, PrivilegedEncoder, RMAConfig +from .adaptation_module import AdaptationModule, AdaptationTrainer, HistoryBuffer diff --git a/rsl_rl/unilab_rma/actor_critic_rma.py b/rsl_rl/unilab_rma/actor_critic_rma.py new file mode 100644 index 000000000..aa20c6a88 --- /dev/null +++ b/rsl_rl/unilab_rma/actor_critic_rma.py @@ -0,0 +1,81 @@ +from dataclasses import asdict, dataclass + +import torch +from torch import nn +from torch.distributions import Normal + + +@dataclass +class RMAConfig: + obs_dim: int = 49 + critic_dim: int = 52 + privileged_dim: int = 2 # sliding friction coefficient, added base mass (kg) + history_length: int = 50 + latent_dim: int = 8 + action_dim: int = 12 + hidden: tuple = (256, 128) + + +def mlp(widths, final=None): + layers = [] + for i, (a, b) in enumerate(zip(widths, widths[1:])): + layers.append(nn.Linear(a, b)) + if i < len(widths) - 2: + layers.append(nn.ELU()) + if final is not None: + layers.append(final) + return nn.Sequential(*layers) + + +class PrivilegedEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + # Fixed scales serialized in checkpoint. No unexported running statistics. + self.register_buffer("scale", torch.tensor([1.0, 5.0])) + if cfg.privileged_dim != 2: + raise ValueError("This contract requires friction and added base mass") + self.net = mlp([2, 64, 64, cfg.latent_dim], nn.Tanh()) + + def forward(self, privileged): + return self.net(privileged / self.scale) + + +class Actor(nn.Module): + def __init__(self, cfg): + super().__init__() + self.net = mlp([cfg.obs_dim + cfg.latent_dim, *cfg.hidden, cfg.action_dim]) + + def forward(self, obs, z): + # Pre-tanh Gaussian mean; squashing is explicit in training and deployment. + return self.net(torch.cat((obs, z), -1)) + + +class ActorCriticRMA(nn.Module): + def __init__(self, cfg=RMAConfig()): + super().__init__() + self.cfg = cfg + self.encoder = PrivilegedEncoder(cfg) + self.actor = Actor(cfg) + self.critic = mlp([cfg.critic_dim + cfg.privileged_dim, *cfg.hidden, 1]) + self.log_std = nn.Parameter(torch.full((cfg.action_dim,), -0.7)) + + def value(self, critic_obs, privileged): + return self.critic(torch.cat((critic_obs, privileged / self.encoder.scale), -1)).squeeze(-1) + + def distribution(self, obs, privileged): + mean = self.actor(obs, self.encoder(privileged)) + return Normal(mean, self.log_std.clamp(-5, 1).exp()) + + @staticmethod + def log_prob(dist, raw): + # Numerically stable log(1 - tanh(raw)^2). + correction = 2 * (0.6931471805599453 - raw - nn.functional.softplus(-2 * raw)) + return (dist.log_prob(raw) - correction).sum(-1) + + def sample(self, obs, privileged): + dist = self.distribution(obs, privileged) + raw = dist.sample() + return raw.tanh(), raw, self.log_prob(dist, raw) + + def metadata(self): + return asdict(self.cfg) diff --git a/rsl_rl/unilab_rma/adaptation_module.py b/rsl_rl/unilab_rma/adaptation_module.py new file mode 100644 index 000000000..bd8148f6e --- /dev/null +++ b/rsl_rl/unilab_rma/adaptation_module.py @@ -0,0 +1,77 @@ +import torch +from torch import nn + +from .actor_critic_rma import mlp + + +class AdaptationModule(nn.Module): + """Time-major history: oldest first, current observation last; zero padding.""" + + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.net = mlp([cfg.history_length * cfg.obs_dim, 256, 128, cfg.latent_dim], nn.Tanh()) + + def forward(self, history): + return self.net(history.flatten(1)) + + +class HistoryBuffer: + def __init__(self, n, cfg, device="cpu"): + self.data = torch.zeros(n, cfg.history_length, cfg.obs_dim, device=device) + + def push(self, obs, done=None): + if done is not None: + self.data[done.bool()] = 0 + self.data[:, :-1] = self.data[:, 1:].clone() + self.data[:, -1] = obs + return self.data + + +class AdaptationTrainer: + """DAgger-style latent labels on STUDENT-visited states, aggregated FIFO data. + + Teacher and actor are fixed in phase 2; only adaptation parameters update. + """ + + def __init__(self, teacher, module, capacity=8192, lr=1e-4, seed=0): + self.teacher = teacher.eval().requires_grad_(False) + self.module = module + self.optimizer = torch.optim.Adam(module.parameters(), lr=lr) + cfg = module.cfg + self.capacity, self.size, self.cursor = capacity, 0, 0 + self.histories = torch.empty(capacity, cfg.history_length, cfg.obs_dim) + self.labels = torch.empty(capacity, cfg.latent_dim) + self.generator = torch.Generator().manual_seed(seed) + + @torch.no_grad() + def add(self, history, privileged): + labels = self.teacher.encoder(privileged) + h, z = history.detach().cpu(), labels.detach().cpu() + if len(h) > self.capacity: + h, z = h[-self.capacity :], z[-self.capacity :] + for start in range(0, len(h), self.capacity): + count = min(len(h) - start, self.capacity) + indices = (torch.arange(count) + self.cursor) % self.capacity + self.histories[indices] = h[start : start + count] + self.labels[indices] = z[start : start + count] + self.cursor = (self.cursor + count) % self.capacity + self.size = min(self.size + count, self.capacity) + + def update(self, batch_size=256, updates=4): + if not self.size: + raise RuntimeError("Collect student rollouts first") + device = next(self.module.parameters()).device + losses = [] + for _ in range(updates): + indices = torch.randint( + self.size, (min(batch_size, self.size),), generator=self.generator + ) + prediction = self.module(self.histories[indices].to(device)) + loss = nn.functional.mse_loss(prediction, self.labels[indices].to(device)) + self.optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(self.module.parameters(), 1.0) + self.optimizer.step() + losses.append(loss.item()) + return sum(losses) / len(losses) diff --git a/rsl_rl/unilab_rma/control_diagnostics.py b/rsl_rl/unilab_rma/control_diagnostics.py new file mode 100644 index 000000000..e0c6eb3eb --- /dev/null +++ b/rsl_rl/unilab_rma/control_diagnostics.py @@ -0,0 +1,161 @@ +"""Evaluation-only reads of public position-control signals, for one world.""" + +import csv +import json +from pathlib import Path + +import numpy as np +import torch + + +def array(value): + if isinstance(value, torch.Tensor): + value = value.detach().cpu().numpy() + return np.array(value, dtype=np.float64, copy=True) + + +class ControlDiagnostics: + def __init__(self, raw, directory): + if raw.num_envs != 1: + raise ValueError("Control diagnostics require exactly one environment") + self.raw = raw + self.directory = Path(directory) + self.term = raw.action_manager.get_term("joint_pos") + self.robot = raw.scene[self.term.cfg.entity_name] + self.names = list(self.term.target_names) + joint_names = list(self.robot.joint_names) + self.joint_ids = [joint_names.index(name) for name in self.names] + actuator_names = list(raw.backend.get_actuator_names()) + actuator_joints = list(raw.backend.get_actuator_joint_names()) + self.actuator_ids = [actuator_joints.index(name) for name in self.names] + if list(self.robot.actuator_names) != actuator_names: + raise ValueError("Entity and backend actuator order differ; cannot interpret buffer") + if len(self.names) != 12 or len(set(self.actuator_ids)) != 12: + raise ValueError("Requires 12 distinct single-joint position actuators") + self.rows = [] + self.step = self.episode = self.age = 0 + self.metadata = { + "joint_names": self.names, + "actuator_names": actuator_names, + "action_to_actuator_indices": self.actuator_ids, + "control_period_s": float(raw.step_dt), + "alignment": "q_before/dq_before precede action; target is the authoritative control buffer after step; q_after precedes reset. No cross-reset rows.", + "mapping_residual": "written target - (processed_action - encoder_bias), in joint order", + "pd_demand_estimate": "kp*(written_target-q_before)-kd*dq_before; an algebraic estimate at the control boundary, NOT measured torque or a physics-substep sample.", + "torque_measurement": "unavailable through the current public backend API; no torque saturation claim", + "gains_scope": "public get_actuator_gains, refreshed each step; one environment only", + } + (self.directory / "control_metadata.json").write_text( + json.dumps(self.metadata, indent=2), encoding="utf-8" + ) + + def before(self): + return ( + array(self.robot.data.joint_pos)[0, self.joint_ids], + array(self.robot.data.joint_vel)[0, self.joint_ids], + ) + + def record(self, before, action, terminated, truncated): + q, dq = before + after = array(self.robot.data.joint_pos)[0, self.joint_ids] + written = array(self.robot.data.control_buffer)[0, self.actuator_ids] + processed = array(self.term.processed_action)[0] + bias = array(self.robot.data.encoder_bias)[0, self.joint_ids] + kp, kd = self.raw.backend.get_actuator_gains() + kp, kd = array(kp).reshape(-1)[self.actuator_ids], array(kd).reshape(-1)[self.actuator_ids] + a = array(action)[0] + self.age += 1 + for i, name in enumerate(self.names): + self.rows.append( + dict( + step=self.step, + episode=self.episode, + episode_step=self.age, + time_s=self.step * float(self.raw.step_dt), + joint=name, + action=a[i], + q_before_rad=q[i], + dq_before_rad_s=dq[i], + q_after_rad=after[i], + processed_target_rad=processed[i], + encoder_bias_rad=bias[i], + written_target_rad=written[i], + mapping_residual_rad=written[i] - (processed[i] - bias[i]), + kp=kp[i], + kd=kd[i], + pd_demand_estimate_nm=kp[i] * (written[i] - q[i]) - kd[i] * dq[i], + terminated=int(terminated), + truncated=int(truncated), + ) + ) + self.step += 1 + if terminated or truncated: + self.episode += 1 + self.age = 0 + + def finish(self): + if not self.rows: + raise ValueError("No control samples") + with (self.directory / "control_signals.csv").open( + "w", newline="", encoding="utf-8" + ) as stream: + writer = csv.DictWriter(stream, fieldnames=list(self.rows[0])) + writer.writeheader() + writer.writerows(self.rows) + report = { + "max_abs_mapping_residual_rad": max(abs(r["mapping_residual_rad"]) for r in self.rows), + "torque_measurement": self.metadata["torque_measurement"], + "samples": len(self.rows), + "joints_after_first_100_steps_per_episode": {}, + } + for name in self.names: + rows = [r for r in self.rows if r["joint"] == name and r["episode_step"] > 100] + report["joints_after_first_100_steps_per_episode"][name] = { + "samples": len(rows), + "position_error_rmse_rad": float( + np.sqrt( + np.mean([(r["written_target_rad"] - r["q_after_rad"]) ** 2 for r in rows]) + ) + ) + if rows + else None, + } + (self.directory / "control_summary.json").write_text( + json.dumps(report, indent=2), encoding="utf-8" + ) + self.plot() + return report + + def plot(self): + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + fig, axes = plt.subplots(4, 3, figsize=(14, 11), constrained_layout=True) + for index, name in enumerate(self.names): + ax = axes.flat[index] + for ep in sorted({r["episode"] for r in self.rows}): + rows = [r for r in self.rows if r["joint"] == name and r["episode"] == ep] + t = [(r["step"] + 1) * float(self.raw.step_dt) for r in rows] + ax.plot( + t, + [r["written_target_rad"] for r in rows], + color="#1761A0", + label="Written target" if ep == 0 else None, + ) + ax.plot( + t, + [r["q_after_rad"] for r in rows], + color="#D36C22", + label="Measured after step" if ep == 0 else None, + ) + ax.set_title(name) + ax.set_ylabel("Angle (rad)") + ax.grid(alpha=0.25) + axes[0, 0].legend(fontsize=8) + for ax in axes[-1]: + ax.set_xlabel("Evaluation time (s)") + fig.suptitle("Authoritative actuator target vs measured angle; episodes plotted separately") + fig.savefig(self.directory / "control_tracking.png", dpi=140) + plt.close(fig) diff --git a/rsl_rl/unilab_rma/deployment.py b/rsl_rl/unilab_rma/deployment.py new file mode 100644 index 000000000..8daf37abd --- /dev/null +++ b/rsl_rl/unilab_rma/deployment.py @@ -0,0 +1,30 @@ +from torch import nn + +from .actor_critic_rma import Actor +from .adaptation_module import AdaptationModule + + +def go2_control_layout(): + """Current task contract, checked against runtime; not stored in old weights.""" + return { + "action_joint_order": ["FL", "FR", "RL", "RR"], + "scene_actuator_order": ["FR", "FL", "RR", "RL"], + "action_to_actuator_indices": [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8], + "control_layout_source": ( + "Current Go2 YAML and Entity.find_joints_by_actuator_names natural joint order; " + "verified in the original 1.3.3 runtime. Historical checkpoints do not store " + "this layout; confirm against a trace from the matching scene before deployment." + ), + } + + +class DeploymentPolicy(nn.Module): + """Only student adaptation and base actor. No teacher encoder or critic.""" + + def __init__(self, cfg): + super().__init__() + self.adaptation = AdaptationModule(cfg) + self.actor = Actor(cfg) + + def forward(self, observation, history): + return self.actor(observation, self.adaptation(history)).tanh() diff --git a/rsl_rl/unilab_rma/env_adapter.py b/rsl_rl/unilab_rma/env_adapter.py new file mode 100644 index 000000000..25d76ec8c --- /dev/null +++ b/rsl_rl/unilab_rma/env_adapter.py @@ -0,0 +1,57 @@ +"""UniLab manual-reset adapter: retain terminal obs before resetting histories.""" + +import torch + + +class UniLabRMAEnv: + def __init__(self, env, cfg, device="cpu"): + self.env, self.cfg, self.device = env, cfg, torch.device(device) + if env._autoreset: + raise ValueError("RMA adapter requires env.auto_reset=false") + self.num_envs = env.num_envs + + def read(self, observations): + if not hasattr(self.env, "_rma_label_valid") or not self.env._rma_label_valid.all(): + raise RuntimeError("RMA reset events must populate actual friction and payload labels") + # ManagerBasedRlEnv exports policy as 'obs' and drops other manager + # groups except the explicitly configured critic group. + policy_key = "obs" if "obs" in observations else "policy" + if policy_key not in observations or "critic" not in observations: + raise KeyError( + f"RMA requires public obs/critic tensors; got {list(observations)}. " + "Set critic_observation_group=critic in the RMA overlay." + ) + if "privileged" in observations: + privileged = observations["privileged"] + else: + # These are the SAME reset samples written to the physics backend. + # Clone immediately: partial resets will overwrite these label rows. + privileged = self.env._rma_privileged + result = tuple( + torch.as_tensor(value, device=self.device, dtype=torch.float32).clone() + for value in (observations[policy_key], observations["critic"], privileged) + ) + for tensor, width in zip( + result, (self.cfg.obs_dim, self.cfg.critic_dim, self.cfg.privileged_dim) + ): + if tensor.shape != (self.num_envs, width) or not torch.isfinite(tensor).all(): + raise ValueError( + f"RMA observation mismatch: {tensor.shape}, expected width {width}" + ) + return result + + def reset(self): + obs, _ = self.env.reset() + return self.read(obs) + + def step(self, action): + state = self.env.step(action.to(self.env.device)) + terminal = self.read(state.obs) + reward = state.reward.to(self.device).clone() + terminated = state.terminated.to(self.device).bool().clone() + truncated = state.truncated.to(self.device).bool().clone() + done = terminated | truncated + if done.any(): + self.env.reset(done.nonzero(as_tuple=False).flatten().to(self.env.device)) + current = self.read(self.env.get_observations()) + return current, reward, terminated, truncated, terminal diff --git a/rsl_rl/unilab_rma/evaluation_diagnostics.py b/rsl_rl/unilab_rma/evaluation_diagnostics.py new file mode 100644 index 000000000..d63b877d4 --- /dev/null +++ b/rsl_rl/unilab_rma/evaluation_diagnostics.py @@ -0,0 +1,85 @@ +"""Opt-in command control and evaluation-only locomotion diagnostics.""" + +import math + +import torch + +DEFAULT_TASK = "go2_joystick_flat/rma" +FORWARD_TASK = "go2_joystick_flat/rma_forward" +NO_POSE_TASK = "go2_joystick_flat/rma_forward_no_pose" +TIGHT_TRACKING_TASK = "go2_joystick_flat/rma_forward_tight_tracking" +RMA_TASKS = (DEFAULT_TASK, FORWARD_TASK, NO_POSE_TASK, TIGHT_TRACKING_TASK) + + +def command_overrides(command): + """Fix commands through their config owner, including after every reset.""" + if len(command) != 3 or not all(math.isfinite(float(x)) for x in command): + raise ValueError("command must contain finite vx, vy, and yaw-rate values") + prefix = "env.commands.twist." + overrides = [ + prefix + f"ranges.{name}=[{float(value)},{float(value)}]" + for name, value in zip(("lin_vel_x", "lin_vel_y", "ang_vel_z"), command) + ] + overrides += [prefix + "heading_command=false"] + overrides += [ + prefix + name + "=0.0" + for name in ("rel_standing_envs", "rel_heading_envs", "rel_world_envs", "rel_forward_envs") + ] + return overrides + + +class EvaluationDiagnostics: + """Accumulate clean critic velocities; never alter actions or observations.""" + + def __init__(self): + self.count = 0 + self.command_sum = torch.zeros(3, dtype=torch.float64) + self.measured_sum = torch.zeros(3, dtype=torch.float64) + self.error_squared_sum = torch.zeros(3, dtype=torch.float64) + self.absolute_yaw_sum = 0.0 + self.initial_command = None + self.reward_sum = {} + self.reward_steps = 0 + + def record_state(self, critic): + # critic[42:45] is command; [49:52] is clean local_linvel; + # critic[0:3] is clean gyro. Policy gyro includes training noise. + state = critic.detach().to(device="cpu", dtype=torch.float64) + if state.ndim != 2 or state.shape[1] != 52: + raise ValueError("Diagnostics require the current 52-dimensional Go2 critic contract") + command = state[:, 42:45] + measured = torch.cat((state[:, 49:51], state[:, 2:3]), dim=1) + if self.initial_command is None: + self.initial_command = command[0].tolist() + self.command_sum += command.sum(0) + self.measured_sum += measured.sum(0) + self.error_squared_sum += (command - measured).square().sum(0) + self.absolute_yaw_sum += measured[:, 2].abs().sum().item() + self.count += len(state) + + def record_rewards(self, reward_manager): + # Public API: already weighted, but NOT multiplied by the control dt. + means = reward_manager.step_reward_means.detach().cpu().tolist() + for name, mean in zip(reward_manager.step_reward_names, means, strict=True): + self.reward_sum[name] = self.reward_sum.get(name, 0.0) + mean + self.reward_steps += 1 + + def report(self): + count = self.count + return { + "velocity_axes": ["vx_m_per_s", "vy_m_per_s", "yaw_rate_rad_per_s"], + "velocity_frame": "robot body; clean critic local_linvel xy and gyro z", + "initial_command_env0": self.initial_command, + "mean_command": (self.command_sum / count).tolist() if count else None, + "mean_measured_velocity": (self.measured_sum / count).tolist() if count else None, + "tracking_rmse_by_axis": (self.error_squared_sum / count).sqrt().tolist() + if count + else None, + "mean_absolute_yaw_rate_rad_per_s": self.absolute_yaw_sum / count if count else None, + "weighted_reward_terms_before_dt": { + name: total / self.reward_steps for name, total in self.reward_sum.items() + } + if self.reward_steps + else {}, + "reward_terms_note": "weighted terms BEFORE control-dt scaling; not per-step rewards", + } diff --git a/rsl_rl/unilab_rma/evaluation_trace.py b/rsl_rl/unilab_rma/evaluation_trace.py new file mode 100644 index 000000000..eb50f8263 --- /dev/null +++ b/rsl_rl/unilab_rma/evaluation_trace.py @@ -0,0 +1,282 @@ +"""Evaluation-only transition recording; no simulator or policy mutation.""" + +import csv +import hashlib +import json +import math +import subprocess +import tempfile +from datetime import datetime, timezone +from pathlib import Path + +import torch + + +def _json_value(value): + if isinstance(value, torch.Tensor): + return value.detach().cpu().tolist() + return value + + +def control_contract(raw): + """Read resolved public action metadata, never infer action order from obs.""" + term = raw.action_manager.get_term("joint_pos") + robot = raw.scene[term.cfg.entity_name] + return { + "action_type": type(term).__name__, + "joint_observation_order": list(robot.joint_names), + "action_target_joint_order": list(term.target_names), + "action_target_joint_indices": [int(index) for index in term.target_ids], + "scale": _json_value(term.scale), + "offset_rad": _json_value(term.offset), + "clip_config": term.cfg.clip, + "note": ( + "Resolved position-action affine mapping: target = offset + scale * action, " + "before configured clipping and encoder-bias compensation. This is configuration " + "evidence, not a measurement of actuator torque or proof of correct physical response." + ), + } + + +class EvaluationTrace: + """One CSV row per environment transition, unique directory per invocation.""" + + def __init__(self, parent, raw, arguments, checkpoint, *, warmup_steps=100): + self.num_envs = int(arguments["num_envs"]) + self.dt = float(raw.step_dt) + if not math.isfinite(self.dt) or self.dt <= 0 or warmup_steps < 0: + raise ValueError("Trace requires positive control dt and nonnegative warmup steps") + self.warmup_steps = warmup_steps + self.control = control_contract(raw) + if len(self.control["action_target_joint_order"]) != 12: + raise ValueError("Trace requires the 12-action Go2 contract") + self.names = tuple(raw.reward_manager.step_reward_names) + parent = Path(parent).resolve() + parent.mkdir(parents=True, exist_ok=True) + self.directory = Path(tempfile.mkdtemp(prefix="eval_", dir=parent)) + self.episode_ids = [0] * self.num_envs + self.episode_steps = [0] * self.num_envs + self.rows = 0 + self.steps = 0 + self.action_min = torch.full((12,), torch.inf, dtype=torch.float64) + self.action_max = -self.action_min.clone() + self.action_squared_sum = torch.zeros(12, dtype=torch.float64) + self.action_near_bound = torch.zeros(12, dtype=torch.float64) + self.velocity_sum = torch.zeros(3, dtype=torch.float64) + self.error_squared_sum = torch.zeros(3, dtype=torch.float64) + self.window_velocity_sum = torch.zeros(3, dtype=torch.float64) + self.window_error_squared_sum = torch.zeros(3, dtype=torch.float64) + self.window_count = 0 + self.reward_sums = {name: 0.0 for name in self.names} + self._stream = None + fields = ["step", "env_id", "episode_id", "episode_step", "time_s", "next_time_s"] + fields += [f"command_{axis}" for axis in ("vx_m_s", "vy_m_s", "wz_rad_s")] + fields += [f"next_command_{axis}" for axis in ("vx_m_s", "vy_m_s", "wz_rad_s")] + fields += [ + f"{phase}_{axis}" + for phase in ("before", "after") + for axis in ("vx_m_s", "vy_m_s", "wz_rad_s") + ] + fields += ["reward", "terminated", "truncated", "after_startup_window"] + fields += [f"action_{i}" for i in range(12)] + fields += [f"joint_pos_rel_{i}_rad" for i in range(12)] + fields += [f"joint_vel_rel_{i}_rad_s" for i in range(12)] + fields += [f"reward_rate/{name}" for name in self.names] + root = Path(__file__).resolve().parents[2] + try: + revision = ( + subprocess.run( + ["git", "-C", str(root), "rev-parse", "HEAD"], + capture_output=True, + text=True, + check=False, + ).stdout.strip() + or None + ) + dirty = subprocess.run( + ["git", "-C", str(root), "status", "--porcelain"], + capture_output=True, + text=True, + check=False, + ) + dirty = bool(dirty.stdout.strip()) if dirty.returncode == 0 else None + except OSError: + revision, dirty = None, None + metadata = { + "format": "unilab-rma-evaluation-trace-v1", + "created_at_utc": datetime.now(timezone.utc).isoformat(), + "arguments": arguments, + "git_commit": revision, + "git_dirty": dirty, + "checkpoint_sha256": hashlib.sha256(Path(checkpoint).read_bytes()).hexdigest(), + "control_period_s": self.dt, + "warmup_steps_per_episode": warmup_steps, + "velocity_frame": "body; clean critic [49:51] and gyro z [2]", + "transition_alignment": ( + "before = s_t; action = a_t; after = terminal s_(t+1), before any reset. " + "Commands at both endpoints are retained; reward belongs to this transition. " + "Full/window summaries use before velocity and command, matching --diagnostics." + ), + "reward_terms": ( + "Per-environment weighted rates from public get_active_iterable_terms(); " + "before control-dt scaling. reward is the actual returned scalar. " + "Do not compare rates directly with reward or across changed weights." + ), + "action_near_bound_definition": "abs(normalized policy action) >= 0.95; NOT torque saturation", + "control_contract": self.control, + } + source_paths = ( + "scripts/evaluate_rma.py", + "rsl_rl/unilab_rma/evaluation_trace.py", + "rsl_rl/unilab_rma/env_adapter.py", + "src/unilab/envs/mdp/actions/actions.py", + "src/unilab/conf/ppo/task/go2_joystick_flat/base.yaml", + "src/unilab/conf/ppo/task/go2_joystick_flat/mujoco.yaml", + "src/unilab/conf/ppo/task/go2_joystick_flat/rma.yaml", + "src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward.yaml", + "src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_no_pose.yaml", + "src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_tight_tracking.yaml", + ) + metadata["runtime_source_sha256"] = { + name: hashlib.sha256((root / name).read_bytes()).hexdigest() + for name in source_paths + if (root / name).is_file() + } + config_dir = self.directory / "task_source_snapshot" + config_dir.mkdir() + for name in source_paths: + if name.endswith(".yaml") and (root / name).is_file(): + (config_dir / Path(name).name).write_bytes((root / name).read_bytes()) + (self.directory / "run_config.json").write_text( + json.dumps(metadata, indent=2, default=str) + "\n", encoding="utf-8" + ) + self._stream = (self.directory / "transitions.csv").open("w", newline="", encoding="utf-8") + self._csv = csv.DictWriter(self._stream, fieldnames=fields) + self._csv.writeheader() + self._stream.flush() + + def record(self, before, action, terminal, reward, terminated, truncated, reward_manager): + before = before.detach().to(device="cpu", dtype=torch.float64) + after = terminal.detach().to(device="cpu", dtype=torch.float64) + actions = action.detach().to(device="cpu", dtype=torch.float64) + if before.shape != (self.num_envs, 52) or after.shape != before.shape: + raise ValueError("Trace requires clean critic tensors of shape (num_envs, 52)") + if actions.shape != (self.num_envs, 12): + raise ValueError("Trace requires actions of shape (num_envs, 12)") + if tuple(reward_manager.step_reward_names) != self.names: + raise ValueError("Reward terms changed during trace") + measured = torch.cat((before[:, 49:51], before[:, 2:3]), dim=1) + next_measured = torch.cat((after[:, 49:51], after[:, 2:3]), dim=1) + errors = (before[:, 42:45] - measured).square() + rewards = reward.detach().cpu().tolist() + terms = terminated.detach().cpu().tolist() + truncs = truncated.detach().cpu().tolist() + for index in range(self.num_envs): + rates = dict(reward_manager.get_active_iterable_terms(index)) + if set(rates) != set(self.names) or any(len(value) != 1 for value in rates.values()): + raise ValueError("Unexpected public reward-term shape") + self.episode_steps[index] += 1 + age = self.episode_steps[index] + in_window = age > self.warmup_steps + row = dict( + step=self.steps, + env_id=index, + episode_id=self.episode_ids[index], + episode_step=age, + time_s=(age - 1) * self.dt, + next_time_s=age * self.dt, + reward=rewards[index], + terminated=int(terms[index]), + truncated=int(truncs[index]), + after_startup_window=int(in_window), + ) + for prefix, values in ( + ("command", before[index, 42:45]), + ("next_command", after[index, 42:45]), + ("before", measured[index]), + ("after", next_measured[index]), + ): + row.update( + zip( + (f"{prefix}_{axis}" for axis in ("vx_m_s", "vy_m_s", "wz_rad_s")), + values.tolist(), + strict=True, + ) + ) + row.update({f"action_{i}": value for i, value in enumerate(actions[index].tolist())}) + row.update( + { + f"joint_pos_rel_{i}_rad": value + for i, value in enumerate(before[index, 6:18].tolist()) + } + ) + row.update( + { + f"joint_vel_rel_{i}_rad_s": value + for i, value in enumerate(before[index, 18:30].tolist()) + } + ) + for name in self.names: + row[f"reward_rate/{name}"] = rates[name][0] + self.reward_sums[name] += rates[name][0] + self._csv.writerow(row) + if in_window: + self.window_count += 1 + self.window_velocity_sum += measured[index] + self.window_error_squared_sum += errors[index] + if terms[index] or truncs[index]: + self.episode_ids[index] += 1 + self.episode_steps[index] = 0 + self.action_min = torch.minimum(self.action_min, actions.amin(0)) + self.action_max = torch.maximum(self.action_max, actions.amax(0)) + self.action_squared_sum += actions.square().sum(0) + self.action_near_bound += (actions.abs() >= 0.95).sum(0) + self.velocity_sum += measured.sum(0) + self.error_squared_sum += errors.sum(0) + self.rows += self.num_envs + self.steps += 1 + if self.steps % 50 == 0: + self._stream.flush() + + def report(self): + def velocity_report(count, total, squared): + return { + "transitions": count, + "mean_measured_velocity": (total / count).tolist() if count else None, + "tracking_rmse_by_axis": (squared / count).sqrt().tolist() if count else None, + } + + return { + "directory": str(self.directory), + "full": velocity_report(self.rows, self.velocity_sum, self.error_squared_sum), + "after_startup_window": velocity_report( + self.window_count, self.window_velocity_sum, self.window_error_squared_sum + ), + "action_target_joint_order": self.control["action_target_joint_order"], + "normalized_action_min": self.action_min.tolist() if self.rows else None, + "normalized_action_max": self.action_max.tolist() if self.rows else None, + "normalized_action_rms": (self.action_squared_sum / self.rows).sqrt().tolist() + if self.rows + else None, + "normalized_action_near_bound_fraction": (self.action_near_bound / self.rows).tolist() + if self.rows + else None, + "mean_weighted_reward_rates_before_dt": { + name: value / self.rows for name, value in self.reward_sums.items() + } + if self.rows + else {}, + } + + def finish(self, evaluation_report): + result = dict(evaluation_report, trace=self.report()) + (self.directory / "summary.json").write_text( + json.dumps(result, indent=2) + "\n", encoding="utf-8" + ) + self._stream.flush() + return result + + def close(self): + if self._stream is not None: + self._stream.close() + self._stream = None diff --git a/rsl_rl/unilab_rma/experiment_report.py b/rsl_rl/unilab_rma/experiment_report.py new file mode 100644 index 000000000..394df59ac --- /dev/null +++ b/rsl_rl/unilab_rma/experiment_report.py @@ -0,0 +1,118 @@ +"""Build a static, auditable report from selected RMA evaluation and CSV data.""" + +import csv +import json +from pathlib import Path + + +def load_evaluations(root): + """Read published reports; fail on mixed command or malformed comparisons.""" + root = Path(root) + records = [] + for label in ("baseline", "forward_v1"): + for seed in (123, 456, 789): + report = json.loads((root / label / f"seed{seed}.json").read_text()) + diagnostics = report["diagnostics"] + if ( + report["policy"] != "teacher" + or report["seed"] != seed + or diagnostics["mean_command"] != [0.5, 0.0, 0.0] + ): + raise ValueError(f"Unexpected evaluation protocol: {label}, seed {seed}") + if report["total_transitions"] != 8000: + raise ValueError("Comparison expects 8000 total transitions per evaluation") + records.append( + { + "model": label, + "seed": seed, + "target_vx_m_s": 0.5, + "mean_vx_m_s": diagnostics["mean_measured_velocity"][0], + "vx_rmse_m_s": diagnostics["tracking_rmse_by_axis"][0], + "planar_rmse_m_s": report["planar_velocity_rmse_m_per_s"], + "terminations": report["terminations"], + "total_transitions": report["total_transitions"], + } + ) + return records + + +def rolling_mean(values, window=100): + """Trailing mean over available numeric rows, retaining startup samples.""" + output, total = [], 0.0 + for index, value in enumerate(values): + total += value + if index >= window: + total -= values[index - window] + output.append(total / min(index + 1, window)) + return output + + +def build_report(root): + """Generate CSV/PNG outputs without importing or running the simulator.""" + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + root = Path(root) + records = load_evaluations(root) + with (root / "evaluation_summary.csv").open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter(stream, fieldnames=list(records[0])) + writer.writeheader() + writer.writerows(records) + figures = root / "figures" + figures.mkdir(parents=True, exist_ok=True) + fig, axes = plt.subplots(1, 2, figsize=(10, 4), constrained_layout=True) + colors = ("#5C6F91", "#E38B3B") + for index, label in enumerate(("baseline", "forward_v1")): + selected = [row for row in records if row["model"] == label] + for axis, field in zip(axes, ("mean_vx_m_s", "vx_rmse_m_s"), strict=True): + values = [row[field] for row in selected] + axis.bar(index, sum(values) / len(values), color=colors[index], alpha=0.65) + axis.scatter([index - 0.07, index, index + 0.07], values, color="#222222", zorder=3) + axes[0].axhline(0.5, color="#C53434", linestyle="--", label="Command: 0.5 m/s") + axes[0].set( + ylabel="Mean body-frame vx (m/s)", title="Measured forward speed", ylim=(-0.04, 0.56) + ) + axes[0].legend(fontsize=8) + axes[1].set(ylabel="Forward tracking RMSE (m/s)", title="Forward speed error", ylim=(0, 0.56)) + for axis in axes: + axis.set_xticks([0, 1], ["Original teacher", "Forward v1"]) + axis.grid(axis="y", alpha=0.2) + fig.suptitle( + "Same checkpoint per group; dots are evaluation seeds, not training replicates", fontsize=10 + ) + fig.savefig(figures / "velocity_comparison.png", dpi=170) + plt.close(fig) + + with (root / "forward_v1/training_metrics.csv").open() as stream: + rows = list(csv.DictReader(stream)) + iterations = [int(row["iteration"]) for row in rows] + rewards = [float(row["mean_step_reward"]) for row in rows] + fig, axes = plt.subplots(2, 1, figsize=(10, 6), sharex=True, constrained_layout=True) + axes[0].plot(iterations, rewards, alpha=0.18, color="#4679A8", label="Per-update reward") + axes[0].plot( + iterations, rolling_mean(rewards), color="#1D4C79", label="Trailing 100-update mean" + ) + axes[0].set( + ylabel="Mean step reward", title="Forward v1: 64 environments, rollout 24, training seed 42" + ) + completed = [ + (int(row["iteration"]), float(row["mean_episode_return"])) + for row in rows + if row["mean_episode_return"] + ] + axes[1].scatter( + [x for x, _ in completed], [y for _, y in completed], s=5, alpha=0.3, color="#E38B3B" + ) + axes[1].set( + xlabel="Completed PPO updates", + ylabel="Mean completed episode return per update", + title="Only updates containing completed episodes are shown", + ) + for axis in axes: + axis.grid(alpha=0.2) + axes[0].legend(fontsize=8) + fig.savefig(figures / "forward_v1_training.png", dpi=170) + plt.close(fig) + return {"evaluation_reports": len(records), "training_rows": len(rows), "figures": str(figures)} diff --git a/rsl_rl/unilab_rma/ppo.py b/rsl_rl/unilab_rma/ppo.py new file mode 100644 index 000000000..b27155c59 --- /dev/null +++ b/rsl_rl/unilab_rma/ppo.py @@ -0,0 +1,47 @@ +"""Small explicit PPO implementation, independent of external RSL-RL internals.""" + +import torch + + +def gae(rewards, values, next_values, terminated, truncated, gamma=0.99, lam=0.95): + advantage = torch.zeros_like(rewards) + carry = torch.zeros_like(rewards[0]) + for t in reversed(range(len(rewards))): + # Timeout bootstraps from PRE-reset state, but never crosses episode boundary. + delta = rewards[t] + gamma * (~terminated[t]).float() * next_values[t] - values[t] + carry = delta + gamma * lam * (~(terminated[t] | truncated[t])).float() * carry + advantage[t] = carry + return advantage, advantage + values + + +class PPO: + def __init__(self, model, lr=3e-4): + self.model = model + self.optimizer = torch.optim.Adam(model.parameters(), lr=lr) + + def update(self, batch, epochs=5, minibatch=256, clip=0.2): + adv = batch["advantages"] + adv = (adv - adv.mean()) / (adv.std(unbiased=False) + 1e-8) + losses = [] + for _ in range(epochs): + for ids in torch.randperm(len(adv), device=adv.device).split(minibatch): + dist = self.model.distribution(batch["obs"][ids], batch["priv"][ids]) + logp = self.model.log_prob(dist, batch["raw"][ids]) + ratio = (logp - batch["logp"][ids]).exp() + surrogate = torch.minimum( + ratio * adv[ids], ratio.clamp(1 - clip, 1 + clip) * adv[ids] + ) + value = self.model.value(batch["critic"][ids], batch["priv"][ids]) + value_loss = (value - batch["returns"][ids]).square().mean() + # Sample estimate of squashed-policy entropy; base Gaussian entropy is not used. + raw_entropy = dist.rsample() + entropy = -self.model.log_prob(dist, raw_entropy).mean() + loss = -surrogate.mean() + 0.5 * value_loss - 0.001 * entropy + if not torch.isfinite(loss): + raise FloatingPointError("Non-finite PPO loss") + self.optimizer.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) + self.optimizer.step() + losses.append(loss.item()) + return sum(losses) / len(losses) diff --git a/rsl_rl/unilab_rma/rendering.py b/rsl_rl/unilab_rma/rendering.py new file mode 100644 index 000000000..8dcf65811 --- /dev/null +++ b/rsl_rl/unilab_rma/rendering.py @@ -0,0 +1,110 @@ +"""Optional interactive evaluation; imported only when --render is requested. + +Keep policy inference, environment stepping, and metrics in the caller. This +iterator only displays their state and paces playback using wall-clock waits. +""" + +from __future__ import annotations + +import copy +import math +import sys +import threading +import time +from collections.abc import Iterator +from contextlib import contextmanager +from typing import Any + + +@contextmanager +def _passive_viewer(mujoco: Any, model: Any, data: Any): + """Wait for MuJoCo's GUI thread before Python tears down native libraries.""" + before = set(threading.enumerate()) + viewer = mujoco.viewer.launch_passive(model, data) + # In MuJoCo 3.11 close() requests exit but does not join its daemon GUI + # thread. Capture that launch's new thread, without touching older workers. + viewer_threads = [ + thread + for thread in threading.enumerate() + if thread not in before and "_launch_internal" in thread.name + ] + try: + with viewer: + yield viewer + finally: + for thread in viewer_threads: + thread.join(timeout=5.0) + if thread.is_alive(): + raise RuntimeError("MuJoCo viewer did not finish shutting down within 5 seconds") + + +def rendered_steps(env: Any, count: int, *, speed: float = 1.0) -> Iterator[int]: + """Render environment zero through UniLab's physics-snapshot contract. + + The environment must already be reset. Its owner remains responsible for + calling close() in a finally block, including on renderer errors or Ctrl+C. + Rendering never changes ctrl_dt, the actions, or the simulation substeps. + """ + if not math.isfinite(speed) or speed <= 0: + raise ValueError("Playback speed must be finite and positive") + period = float(env.step_dt) / speed + if not math.isfinite(period) or period <= 0: + raise ValueError("Interactive playback requires a finite, positive step_dt") + # MuJoCo's batch backend supports snapshots, NOT init_play_renderer(). + # Use the same playback contract as UniLab's play_interactive entrypoint. + import glfw + import mujoco + import mujoco.viewer + + from unilab.visualization.playback_state import ( + PhysicsStateApplier, + assert_physics_state_playback_supported, + ) + + assert_physics_state_playback_supported(env, entrypoint="evaluate_rma --render") + if not glfw.init(): + raise RuntimeError( + "Cannot open the MuJoCo viewer: no usable GLFW display. " + "In WSL, check WSLg/DISPLAY and OpenGL support. " + "Omit --render to continue normal headless evaluation." + ) + glfw.terminate() + # Neither viewer.sync() nor mouse perturbations may write into the live + # physics model/data: both playback objects belong exclusively to the GUI. + source_model = env.get_playback_model(env_index=0) + if isinstance(source_model, str): + model = ( + mujoco.MjModel.from_binary_path(source_model) + if source_model.lower().endswith(".mjb") + else mujoco.MjModel.from_xml_path(source_model) + ) + else: + model = copy.deepcopy(source_model) + data = mujoco.MjData(model) + applier = PhysicsStateApplier(env, model, env_index=0) + applier.apply(env.get_physics_state_snapshot()[0], data) + print( + f"[render] Showing environment 0 at {speed:g}x speed; " + "close the window or press Ctrl+C to stop.", + file=sys.stderr, + ) + with _passive_viewer(mujoco, model, data) as viewer: + with viewer.lock(): + viewer.cam.distance = 2.5 + viewer.cam.elevation = -20.0 + base_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, "base") + if base_id >= 0: + viewer.cam.type = mujoco.mjtCamera.mjCAMERA_TRACKING + viewer.cam.trackbodyid = base_id + viewer.sync() + for index in range(count): + if not viewer.is_running(): + break + started = time.perf_counter() + yield index + with viewer.lock(): + applier.apply(env.get_physics_state_snapshot()[0], data) + viewer.sync() + remaining = period - (time.perf_counter() - started) + if remaining > 0: + time.sleep(remaining) diff --git a/rsl_rl/unilab_rma/runner.py b/rsl_rl/unilab_rma/runner.py new file mode 100644 index 000000000..5402ba91f --- /dev/null +++ b/rsl_rl/unilab_rma/runner.py @@ -0,0 +1,146 @@ +from dataclasses import asdict +from pathlib import Path + +import torch + +from .adaptation_module import AdaptationTrainer, HistoryBuffer +from .ppo import PPO, gae + + +def checkpoint(path, model, adaptation, iteration, optimizer=None, phase="teacher"): + Path(path).parent.mkdir(parents=True, exist_ok=True) + torch.save( + { + "format": "unilab-rma-v1", + "config": asdict(model.cfg), + "teacher": model.state_dict(), + "adaptation": adaptation.state_dict(), + "iteration": iteration, + "phase": phase, + "optimizer": None if optimizer is None else optimizer.state_dict(), + }, + path, + ) + + +def train_teacher(env, model, adaptation, iterations, path, horizon=24, logger=None): + ppo = PPO(model) + obs, critic, priv = env.reset() + if logger is not None: + from .training_log import EpisodeLog + + episodes = EpisodeLog(env.num_envs) + for iteration in range(iterations): + data = { + k: [] + for k in ( + "obs", + "critic", + "priv", + "raw", + "logp", + "values", + "rewards", + "next_values", + "terminated", + "truncated", + ) + } + for _ in range(horizon): + with torch.no_grad(): + action, raw, logp = model.sample(obs, priv) + value = model.value(critic, priv) + following, reward, term, trunc, terminal = env.step(action) + next_value = model.value(terminal[1], terminal[2]) + entries = dict( + obs=obs, + critic=critic, + priv=priv, + raw=raw, + logp=logp, + values=value, + rewards=reward, + next_values=next_value, + terminated=term, + truncated=trunc, + ) + for key, tensor in entries.items(): + data[key].append(tensor.detach().clone()) + obs, critic, priv = following + batch = {key: torch.stack(value) for key, value in data.items()} + batch["advantages"], batch["returns"] = gae( + batch["rewards"], + batch["values"], + batch["next_values"], + batch["terminated"], + batch["truncated"], + ) + loss = ppo.update({k: v.flatten(0, 1) for k, v in batch.items()}) + print( + f"teacher iteration={iteration} loss={loss:.5f} mean_step_reward={batch['rewards'].mean():.5f}", + flush=True, + ) + if logger is not None: + logger.write( + iteration + 1, + (iteration + 1) * horizon * env.num_envs, + loss=loss, + mean_step_reward=batch["rewards"].mean().item(), + **episodes.consume(batch["rewards"], batch["terminated"] | batch["truncated"]), + ) + if (iteration + 1) % 20 == 0 or iteration + 1 == iterations: + checkpoint(path, model, adaptation, iteration + 1, ppo.optimizer) + + +def train_student(env, teacher, adaptation, iterations, path, horizon=24, logger=None): + trainer = AdaptationTrainer(teacher, adaptation) + obs, _, priv = env.reset() + history = HistoryBuffer(env.num_envs, teacher.cfg, obs.device) + history.push(obs) + episode_returns = torch.zeros(env.num_envs, device=obs.device) + episode_lengths = torch.zeros(env.num_envs, device=obs.device) + for iteration in range(iterations): + completed_returns, completed_lengths, rewards = [], [], [] + for _ in range(horizon): + trainer.add(history.data, priv) + with torch.no_grad(): + # Students collect their OWN state distribution, not teacher rollouts. + action = teacher.actor(obs, adaptation(history.data)).tanh() + (obs, _, priv), reward, term, trunc, _ = env.step(action) + done = term | trunc + episode_returns += reward + episode_lengths += 1 + rewards.append(reward.mean().item()) + completed_returns.extend(episode_returns[done].tolist()) + completed_lengths.extend(episode_lengths[done].tolist()) + episode_returns[done] = 0 + episode_lengths[done] = 0 + history.push(obs, done) + loss = trainer.update() + episodic = ( + f" episode_return={sum(completed_returns) / len(completed_returns):.5f}" + f" episode_length={sum(completed_lengths) / len(completed_lengths):.1f}" + if completed_returns + else " completed_episodes=0" + ) + print( + f"student iteration={iteration} latent_mse={loss:.6f}" + f" mean_step_reward={sum(rewards) / len(rewards):.5f}" + episodic, + flush=True, + ) + if logger is not None: + logger.write( + iteration + 1, + (iteration + 1) * horizon * env.num_envs, + latent_mse=loss, + mean_step_reward=sum(rewards) / len(rewards), + completed_episodes=len(completed_returns), + mean_episode_return=( + sum(completed_returns) / len(completed_returns) if completed_returns else None + ), + mean_episode_length_steps=( + sum(completed_lengths) / len(completed_lengths) if completed_lengths else None + ), + ) + if (iteration + 1) % 20 == 0 or iteration + 1 == iterations: + checkpoint(path, teacher, adaptation, iteration + 1, trainer.optimizer, phase="student") diff --git a/rsl_rl/unilab_rma/scene_contract.py b/rsl_rl/unilab_rma/scene_contract.py new file mode 100644 index 000000000..ae77cce0e --- /dev/null +++ b/rsl_rl/unilab_rma/scene_contract.py @@ -0,0 +1,46 @@ +"""Cold-path XML discovery; never guess missing scene/home/geometry metadata.""" + +import xml.etree.ElementTree as ET +from pathlib import Path + + +def expanded_xml(path, stack=()): + path = Path(path).resolve() + if path in stack: + raise ValueError(f"Cyclic XML include: {path}") + if not path.is_file(): + raise FileNotFoundError(f"Restore original UniLab XML/mesh assets first: {path}") + root = ET.parse(path).getroot() + + def expand(parent): + for child in list(parent): + if child.tag == "include": + included = expanded_xml(path.parent / child.attrib["file"], (*stack, path)) + index = list(parent).index(child) + parent.remove(child) + for offset, element in enumerate(list(included)): + parent.insert(index + offset, element) + else: + expand(child) + + expand(root) + return root + + +def prepare_scene(override, root_dir): + scene = override["scene"] + path = Path(scene["model_file"]) + if not path.is_absolute(): + path = Path(root_dir) / path + tree = expanded_xml(path) + base = next((b for b in tree.iter("body") if b.get("name") == "base"), None) + if base is None: + raise ValueError("Expected the Go2 base body in restored scene") + names = [g.attrib["name"] for g in base.iter("geom") if g.get("name")] + if not names or len(names) != len(set(names)): + raise ValueError( + "RMA requires unique named robot geoms; inspect/name collision geoms in XML" + ) + scene["model_file"] = str(path.resolve()) + scene["entities"]["robot"]["geom_names"] = names + print(f"RMA friction geom selection from XML: {names}", flush=True) diff --git a/rsl_rl/unilab_rma/tensorrt_runtime.py b/rsl_rl/unilab_rma/tensorrt_runtime.py new file mode 100644 index 000000000..b3ecf928a --- /dev/null +++ b/rsl_rl/unilab_rma/tensorrt_runtime.py @@ -0,0 +1,129 @@ +"""Optional C++ inference bridge; no TensorRT Python package is required. + +The worker persists for an evaluation run. Physics, observation/history assembly, +episode resets and optional rendering continue to belong to the UniLab caller. +""" + +from __future__ import annotations + +import os +import select +import struct +import subprocess +import time +from pathlib import Path + +import numpy as np +import torch + + +class TensorRTWorker: + def __init__(self, worker: Path, engine: Path, cfg, *, timeout_s: float = 30.0): + if os.name != "posix": + raise RuntimeError("Run the TensorRT simulation bridge inside Linux/WSL") + if (cfg.obs_dim, cfg.history_length, cfg.action_dim) != (49, 50, 12): + raise ValueError("C++ worker requires observation=49, history=50, action=12") + if not np.isfinite(timeout_s) or timeout_s <= 0: + raise ValueError("Worker timeout must be finite and positive") + worker, engine = Path(worker).resolve(), Path(engine).resolve() + if not worker.is_file(): + raise FileNotFoundError(f"Build with WITH_TENSORRT=ON first: {worker}") + if not engine.is_file(): + raise FileNotFoundError(f"TensorRT engine not found: {engine}") + self.timeout_s = timeout_s + self._process = subprocess.Popen( + [str(worker), "--engine", str(engine)], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=None, + bufsize=0, + ) + self._closed = False + try: + os.set_blocking(self._process.stdin.fileno(), False) + os.set_blocking(self._process.stdout.fileno(), False) + magic, obs, history, action = struct.unpack("<4sIII", self._read_exact(16)) + if (magic, obs, history, action) != (b"RMA1", 49, 50, 12): + raise RuntimeError("TensorRT worker protocol/dimension mismatch") + except BaseException: + self.close() + raise + + def _read_exact(self, size: int) -> bytes: + fd = self._process.stdout.fileno() + deadline = time.monotonic() + self.timeout_s + result = bytearray() + while len(result) < size: + remaining = deadline - time.monotonic() + if remaining <= 0 or not select.select([fd], [], [], remaining)[0]: + raise TimeoutError("TensorRT worker timed out while reading output") + try: + part = os.read(fd, size - len(result)) + except BlockingIOError: + continue + if not part: + raise RuntimeError("TensorRT worker closed output; check its stderr message") + result.extend(part) + return bytes(result) + + def _write_all(self, payload: bytes) -> None: + fd = self._process.stdin.fileno() + pending = memoryview(payload) + deadline = time.monotonic() + self.timeout_s + while pending: + remaining = deadline - time.monotonic() + if remaining <= 0 or not select.select([], [fd], [], remaining)[1]: + raise TimeoutError("TensorRT worker timed out while writing input") + try: + written = os.write(fd, pending) + except BlockingIOError: + continue + except BrokenPipeError as error: + raise RuntimeError( + "TensorRT worker closed input; check its stderr message" + ) from error + pending = pending[written:] + + def infer(self, observation: torch.Tensor, history: torch.Tensor) -> torch.Tensor: + if self._closed: + raise RuntimeError("TensorRT worker is closed") + if observation.ndim != 2 or observation.shape[1] != 49: + raise ValueError("Expected observation shape [num_envs, 49]") + count = observation.shape[0] + if count < 1 or history.shape != (count, 50, 49): + raise ValueError("Expected history shape [num_envs, 50, 49]") + obs = observation.detach().to(device="cpu", dtype=torch.float32).contiguous().numpy() + hist = history.detach().to(device="cpu", dtype=torch.float32).contiguous().numpy() + if not np.isfinite(obs).all() or not np.isfinite(hist).all(): + raise ValueError("TensorRT inputs must be finite") + actions = np.empty((count, 12), dtype=np.float32) + # Fixed-batch-one engine: process each environment independently. Read + # each reply before the next request so pipe capacity cannot deadlock. + for index in range(count): + payload = np.concatenate((obs[index], hist[index].reshape(-1))).astype( + " None: + if self._closed: + return + self._closed = True + process = self._process + try: + process.stdin.close() + try: + process.wait(timeout=1.0) + except subprocess.TimeoutExpired: + process.terminate() + try: + process.wait(timeout=1.0) + except subprocess.TimeoutExpired: + process.kill() + process.wait(timeout=1.0) + finally: + process.stdout.close() diff --git a/rsl_rl/unilab_rma/training_log.py b/rsl_rl/unilab_rma/training_log.py new file mode 100644 index 000000000..04630c06d --- /dev/null +++ b/rsl_rl/unilab_rma/training_log.py @@ -0,0 +1,115 @@ +"""Per-iteration CSV and TensorBoard records, independent of policy updates.""" + +import csv +import json +import tempfile +import time +from datetime import datetime +from pathlib import Path + + +class TrainingLog: + FIELDS = ( + "iteration", + "total_transitions", + "wall_seconds", + "loss", + "latent_mse", + "mean_step_reward", + "completed_episodes", + "mean_episode_return", + "mean_episode_length_steps", + ) + + def __init__(self, output, phase, metadata, log_dir=None): + output = Path(output).resolve() + parent = Path(log_dir).resolve() if log_dir else output.parent / (output.stem + "_logs") + parent.mkdir(parents=True, exist_ok=True) + self.directory = Path( + tempfile.mkdtemp(prefix=datetime.now().strftime("%Y%m%d_%H%M%S_"), dir=parent) + ) + self._writer = None + self._stream = None + self._started = time.monotonic() + self._phase = phase + try: + from torch.utils.tensorboard import SummaryWriter + + self._writer = SummaryWriter(log_dir=str(self.directory / "tensorboard"), flush_secs=10) + self._stream = (self.directory / "metrics.csv").open("w", newline="", encoding="utf-8") + self._csv = csv.DictWriter(self._stream, fieldnames=self.FIELDS) + self._csv.writeheader() + self._stream.flush() + record = dict( + metadata, + output=str(output), + phase=phase, + log_directory=str(self.directory), + iteration_numbering="one-based completed updates", + ) + (self.directory / "run_config.json").write_text( + json.dumps(record, indent=2, default=str) + "\n", encoding="utf-8" + ) + except BaseException: + self.close() + raise + + def write(self, iteration, total_transitions, **metrics): + row = dict( + iteration=iteration, + total_transitions=total_transitions, + wall_seconds=time.monotonic() - self._started, + **metrics, + ) + self._csv.writerow(row) + self._stream.flush() # Keep completed iterations available after interruption. + for name, value in row.items(): + if value is not None and name != "iteration": + self._writer.add_scalar(self._phase + "/" + name, value, iteration) + + def close(self): + try: + if self._writer is not None: + self._writer.close() + self._writer = None + finally: + if self._stream is not None: + self._stream.close() + self._stream = None + + def __enter__(self): + return self + + def __exit__(self, *args): + self.close() + + +class EpisodeLog: + """CPU-only bookkeeping; consumes detached rollout copies once per update.""" + + def __init__(self, count): + self.returns = [0.0] * count + self.lengths = [0] * count + + def consume(self, rewards, done): + completed_returns, completed_lengths = [], [] + for reward_row, done_row in zip( + rewards.detach().cpu().tolist(), done.detach().cpu().tolist() + ): + for index, (reward, finished) in enumerate(zip(reward_row, done_row)): + self.returns[index] += reward + self.lengths[index] += 1 + if finished: + completed_returns.append(self.returns[index]) + completed_lengths.append(self.lengths[index]) + self.returns[index] = 0.0 + self.lengths[index] = 0 + return dict( + completed_episodes=len(completed_returns), + mean_episode_return=( + sum(completed_returns) / len(completed_returns) if completed_returns else None + ), + mean_episode_length_steps=( + sum(completed_lengths) / len(completed_lengths) if completed_lengths else None + ), + ) diff --git a/scripts/check_rma_mapping.py b/scripts/check_rma_mapping.py new file mode 100644 index 000000000..97955f032 --- /dev/null +++ b/scripts/check_rma_mapping.py @@ -0,0 +1,58 @@ +"""Check nonzero joint targets against actual actuator storage without stepping physics.""" + +import argparse +import json + +import torch +from train_rma import build_environment +from unilab_rma import RMAConfig +from unilab_rma.evaluation_diagnostics import FORWARD_TASK + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.parse_args() + raw, env = build_environment(2, RMAConfig(), "cpu", 123, task=FORWARD_TASK) + try: + env.reset() + robot = raw.scene["robot"] + joints = list(robot.joint_names) + actuator_joints = list(raw.backend.get_actuator_joint_names()) + indices = [actuator_joints.index(name) for name in joints] + control = robot.data.control_buffer + if not isinstance(control, torch.Tensor) or len(joints) != 12: + raise ValueError("Expected the 12-joint Torch Go2 control plane") + errors, equivalence = [], [] + for after_reset in (False, True): + if after_reset: + raw.reset(torch.tensor([0], device=raw.device)) + saved = control.clone() + try: + for column in range(12): + target = torch.zeros((2, 12), dtype=control.dtype, device=control.device) + target[0, column] = 0.1 + target[1, column] = -0.1 + robot.set_full_width_joint_position_target(target) + actual = control.clone() + errors.append((actual[:, indices] - target).abs().max().item()) + robot.set_joint_position_target(target) + equivalence.append((actual - control).abs().max().item()) + finally: + control.copy_(saved) + report = dict( + cases=len(errors), + num_envs=2, + partial_reset_checked=True, + max_abs_mapping_error_rad=max(errors), + max_abs_fast_vs_ordinary_error_rad=max(equivalence), + physics_steps=0, + ) + print(json.dumps(report, indent=2)) + if max(errors + equivalence) > 1e-6: + parser.exit(1, "Mapping check FAILED; do not start a new training run.\n") + finally: + raw.close() + + +if __name__ == "__main__": + main() diff --git a/scripts/diagnose_rma_control.py b/scripts/diagnose_rma_control.py new file mode 100644 index 000000000..7759b1566 --- /dev/null +++ b/scripts/diagnose_rma_control.py @@ -0,0 +1,92 @@ +"""Short single-world teacher control diagnosis; never updates model weights.""" + +import argparse +import hashlib +import json +from pathlib import Path + +import torch +from train_rma import build_environment +from unilab_rma import ActorCriticRMA, AdaptationModule, RMAConfig +from unilab_rma.control_diagnostics import ControlDiagnostics +from unilab_rma.evaluation_diagnostics import RMA_TASKS, EvaluationDiagnostics +from unilab_rma.evaluation_trace import EvaluationTrace + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--checkpoint", type=Path, required=True) + p.add_argument("--task", choices=RMA_TASKS, required=True) + p.add_argument("--steps", type=int, default=500) + p.add_argument("--seed", type=int, default=123) + p.add_argument("--command", nargs=3, type=float, default=[0.5, 0.0, 0.0]) + p.add_argument("--output-dir", type=Path, default=Path("runs/rma/control_diagnostics")) + args = p.parse_args() + if args.steps < 1 or args.steps > 5000: + p.error("steps must be between 1 and 5000 for this short diagnostic") + saved = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + if saved.get("format") != "unilab-rma-v1" or saved.get("phase") != "teacher": + p.error("This diagnostic requires a teacher checkpoint") + torch.manual_seed(args.seed) + cfg = RMAConfig(**saved["config"]) + model = ActorCriticRMA(cfg).eval() + # The reference evaluator initializes this even in teacher mode. Preserve + # its RNG consumption so environment observations have the same sequence. + AdaptationModule(cfg).eval() + model.load_state_dict(saved["teacher"]) + raw, env = build_environment(1, cfg, "cpu", args.seed, task=args.task, command=args.command) + trace = None + try: + obs, critic, priv = env.reset() + trace = EvaluationTrace(args.output_dir, raw, dict(vars(args), num_envs=1), args.checkpoint) + recorder = ControlDiagnostics(raw, trace.directory) + diagnostics = EvaluationDiagnostics() + with torch.no_grad(): + for _ in range(args.steps): + before = recorder.before() + diagnostics.record_state(critic) + action = model.actor(obs, model.encoder(priv)).tanh() + # Read final controls/physics BEFORE any explicit episode reset. + state = raw.step(action.to(raw.device)) + terminal = env.read(state.obs) + term, trunc = bool(state.terminated[0]), bool(state.truncated[0]) + recorder.record(before, action, term, trunc) + trace.record( + critic, + action, + terminal[1], + state.reward, + state.terminated, + state.truncated, + raw.reward_manager, + ) + diagnostics.record_rewards(raw.reward_manager) + if term or trunc: + raw.reset(torch.tensor([0], device=raw.device)) + # Match UniLabRMAEnv.step, including observation refresh/noise. + obs, critic, priv = env.read(raw.get_observations()) + control = recorder.finish() + report = trace.finish( + dict( + policy="teacher", + seed=args.seed, + task=args.task, + total_transitions=args.steps, + diagnostics=diagnostics.report(), + control=control, + ) + ) + source = Path(__file__) + report["diagnostic_source_sha256"] = hashlib.sha256(source.read_bytes()).hexdigest() + (trace.directory / "diagnostic_summary.json").write_text( + json.dumps(report, indent=2), encoding="utf-8" + ) + print(json.dumps(dict(directory=str(trace.directory), **control), indent=2)) + finally: + if trace is not None: + trace.close() + raw.close() + + +if __name__ == "__main__": + main() diff --git a/scripts/evaluate_rma.py b/scripts/evaluate_rma.py new file mode 100644 index 000000000..1c6055d24 --- /dev/null +++ b/scripts/evaluate_rma.py @@ -0,0 +1,248 @@ +"""Evaluate RMA policies; opt into interactive playback with --render.""" + +import argparse +import json +import math +from pathlib import Path + +import torch +from train_rma import build_environment +from unilab_rma import ActorCriticRMA, AdaptationModule, HistoryBuffer, RMAConfig +from unilab_rma.evaluation_diagnostics import DEFAULT_TASK, RMA_TASKS, EvaluationDiagnostics + + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--checkpoint", type=Path, required=True) + p.add_argument("--policy", choices=["teacher", "student"], default="student") + p.add_argument("--steps", type=int, default=1000) + p.add_argument("--num-envs", type=int, default=8) + p.add_argument("--seed", type=int, default=123) + p.add_argument("--device", default="cpu") + p.add_argument("--task", choices=RMA_TASKS, default=DEFAULT_TASK) + p.add_argument( + "--command", + nargs=3, + type=float, + metavar=("VX", "VY", "WZ"), + help="Fix body-frame vx, vy (m/s), yaw rate (rad/s), including after reset", + ) + p.add_argument( + "--diagnostics", + action="store_true", + help="Report commanded/actual velocity and weighted reward terms", + ) + p.add_argument( + "--trace-dir", + type=Path, + help="Opt in to per-transition CSV and JSON in a unique subdirectory of this parent", + ) + p.add_argument( + "--trace-warmup-steps", + type=int, + default=100, + help="Exclude this many initial steps per episode from the additional trace window summary", + ) + p.add_argument( + "--backend", + choices=["pytorch", "tensorrt"], + default="pytorch", + help="Inference backend; TensorRT uses a persistent C++ worker", + ) + p.add_argument( + "--engine", type=Path, help="TensorRT engine exported from this student checkpoint" + ) + p.add_argument( + "--tensorrt-worker", + type=Path, + default=Path(__file__).resolve().parents[1] / "build/deploy_trt/policy_worker", + ) + p.add_argument( + "--render", + action="store_true", + help="Show interactive playback; omitted by default for fast evaluation", + ) + p.add_argument( + "--render-speed", + type=float, + default=1.0, + help="Playback speed with --render: 1.0 is real time, 0.5 is half speed", + ) + args = p.parse_args() + if args.steps < 1 or args.num_envs < 1: + p.error("steps and num-envs must be positive") + if args.trace_warmup_steps < 0: + p.error("trace-warmup-steps must be nonnegative") + if not math.isfinite(args.render_speed) or args.render_speed <= 0: + p.error("render-speed must be finite and positive") + if args.command is not None and not all(math.isfinite(x) for x in args.command): + p.error("--command requires finite vx, vy, and yaw rate") + if args.backend == "tensorrt" and (args.engine is None or args.policy != "student"): + p.error("TensorRT requires --policy student and --engine matching the checkpoint") + if args.backend != "tensorrt" and args.engine is not None: + p.error("--engine requires --backend tensorrt") + saved = torch.load(args.checkpoint, map_location=args.device, weights_only=True) + if saved.get("format") != "unilab-rma-v1": + raise ValueError("Not an RMA checkpoint") + if args.policy == "student" and saved.get("phase") != "student": + raise ValueError("Student evaluation requires an adaptation-trained checkpoint") + torch.manual_seed(args.seed) + cfg = RMAConfig(**saved["config"]) + model = ActorCriticRMA(cfg).to(args.device).eval() + adaptation = AdaptationModule(cfg).to(args.device).eval() + model.load_state_dict(saved["teacher"]) + adaptation.load_state_dict(saved["adaptation"]) + environment_options = {} + if args.task != DEFAULT_TASK: + environment_options["task"] = args.task + if args.command is not None: + environment_options["command"] = args.command + raw, env = build_environment(args.num_envs, cfg, args.device, args.seed, **environment_options) + returns = torch.zeros(args.num_envs, device=args.device) + completed, completed_lengths, tracking = [], [], [] + partial_returns = torch.zeros(args.num_envs, device=args.device) + partial_lengths = torch.zeros(args.num_envs, device=args.device) + termination_count, step_rewards = 0, [] + latent_errors, action_errors = [], [] + steps = range(args.steps) + playback = None + runtime = None + trace = None + backend_action_errors, backend_max_abs_error = [], 0.0 + diagnostics = EvaluationDiagnostics() if args.diagnostics else None + try: + if args.backend == "tensorrt": + from unilab_rma.tensorrt_runtime import TensorRTWorker + + runtime = TensorRTWorker(args.tensorrt_worker, args.engine, cfg) + obs, critic, priv = env.reset() + if args.trace_dir is not None: + from unilab_rma.evaluation_trace import EvaluationTrace + + trace = EvaluationTrace( + args.trace_dir, + raw, + vars(args), + args.checkpoint, + warmup_steps=args.trace_warmup_steps, + ) + history = HistoryBuffer(args.num_envs, cfg, args.device) + history.push(obs) + # The default path retains the original range iterator: no renderer, + # rendering imports, per-step clocks, or playback sleeps. + if args.render: + from unilab_rma.rendering import rendered_steps + + playback = rendered_steps(raw, args.steps, speed=args.render_speed) + steps = playback + with torch.no_grad(): + for _ in steps: + if trace is not None: + trace_before = critic + if diagnostics is not None: + diagnostics.record_state(critic) + tracking.append((critic[:, 49:51] - obs[:, 42:44]).square().sum(-1).mean().item()) + teacher_z = model.encoder(priv) + student_z = adaptation(history.data) + if args.policy == "student": + latent_errors.append((student_z - teacher_z).square().mean().item()) + z = teacher_z if args.policy == "teacher" else student_z + teacher_action = model.actor(obs, teacher_z).tanh() + action = model.actor(obs, z).tanh() + if runtime is not None: + deployed_action = runtime.infer(obs, history.data) + difference = deployed_action - action + backend_action_errors.append(difference.square().mean().item()) + backend_max_abs_error = max( + backend_max_abs_error, difference.abs().max().item() + ) + action = deployed_action + if args.policy == "student": + action_errors.append((action - teacher_action).square().mean().item()) + (obs, critic, priv), reward, term, trunc, terminal = env.step(action) + if trace is not None: + trace.record( + trace_before, action, terminal[1], reward, term, trunc, raw.reward_manager + ) + if diagnostics is not None: + diagnostics.record_rewards(raw.reward_manager) + returns += reward + partial_returns += reward + partial_lengths += 1 + done = term | trunc + completed.extend(returns[done].tolist()) + completed_lengths.extend(partial_lengths[done].tolist()) + returns[done] = 0 + partial_returns[done] = 0 + partial_lengths[done] = 0 + termination_count += int(term.sum()) + step_rewards.append(reward.mean().item()) + history.push(obs, done) + report = { + "policy": args.policy, + "seed": args.seed, + "completed_episodes": len(completed), + "mean_completed_episode_return": sum(completed) / len(completed) if completed else None, + "mean_completed_episode_length_steps": sum(completed_lengths) / len(completed_lengths) + if completed_lengths + else None, + "mean_step_reward": sum(step_rewards) / len(step_rewards) if step_rewards else None, + "planar_velocity_rmse_m_per_s": (sum(tracking) / len(tracking)) ** 0.5 + if tracking + else None, + "unfinished_episodes": int((partial_lengths > 0).sum()), + "mean_unfinished_episode_return": ( + partial_returns[partial_lengths > 0].mean().item() + if (partial_lengths > 0).any() + else None + ), + "student_latent_mse_vs_teacher": ( + sum(latent_errors) / len(latent_errors) if latent_errors else None + ), + "student_action_rmse_vs_teacher": ( + (sum(action_errors) / len(action_errors)) ** 0.5 if action_errors else None + ), + "terminations": termination_count, + "total_transitions": len(step_rewards) * args.num_envs, + } + if args.command is not None: + report["command_override"] = args.command + if args.task != DEFAULT_TASK: + report["task"] = args.task + if diagnostics is not None: + report["diagnostics"] = diagnostics.report() + if runtime is not None: + report.update( + { + "inference_backend": "tensorrt_cpp", + "tensorrt_action_rmse_vs_pytorch": ( + (sum(backend_action_errors) / len(backend_action_errors)) ** 0.5 + if backend_action_errors + else None + ), + "tensorrt_action_max_abs_error_vs_pytorch": ( + backend_max_abs_error if backend_action_errors else None + ), + } + ) + if trace is not None: + report = trace.finish(report) + print(json.dumps(report, indent=2)) + finally: + try: + if playback is not None: + playback.close() # Release the viewer even if policy inference fails. + finally: + try: + if runtime is not None: + runtime.close() + finally: + try: + if trace is not None: + trace.close() + finally: + raw.close() + + +if __name__ == "__main__": + main() diff --git a/scripts/export_onnx.py b/scripts/export_onnx.py new file mode 100644 index 000000000..1d9417e83 --- /dev/null +++ b/scripts/export_onnx.py @@ -0,0 +1,101 @@ +"""Export a STUDENT checkpoint, then require ONNX Runtime numerical validation.""" + +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "rsl_rl")) +import argparse +import json + +import numpy as np +import torch +from unilab_rma import RMAConfig +from unilab_rma.deployment import DeploymentPolicy, go2_control_layout + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + saved = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + if saved.get("format") != "unilab-rma-v1" or saved.get("phase") != "student": + raise ValueError("Export requires an adaptation-trained student checkpoint") + cfg = RMAConfig(**saved["config"]) + module = DeploymentPolicy(cfg).eval() + actor_state = { + k[len("actor.") :]: v for k, v in saved["teacher"].items() if k.startswith("actor.") + } + module.actor.load_state_dict(actor_state, strict=True) + module.adaptation.load_state_dict(saved["adaptation"], strict=True) + args.output.parent.mkdir(parents=True, exist_ok=True) + torch.manual_seed(123) + obs = torch.randn(1, cfg.obs_dim) + hist = torch.randn(1, cfg.history_length, cfg.obs_dim) + hist[:, -1] = obs + # Static batch=1; fixed history=50; FP32 IO, FP16 internal TensorRT build later. + torch.onnx.export( + module, + (obs, hist), + str(args.output), + opset_version=18, + input_names=["observation", "history"], + output_names=["action"], + dynamo=False, + ) + import onnx + import onnxruntime as ort + + graph = onnx.load(str(args.output)) + onnx.checker.check_model(graph) + names = [initializer.name for initializer in graph.graph.initializer] + if any("critic" in name or "encoder" in name for name in names): + raise AssertionError("Privileged teacher leaked into deployment graph") + session = ort.InferenceSession(str(args.output), providers=["CPUExecutionProvider"]) + maximum = 0.0 + for i in range(12): + obs = torch.randn(1, cfg.obs_dim) + hist = torch.randn(1, cfg.history_length, cfg.obs_dim) + if i % 3 == 0: + hist.zero_() # episode-start padding + hist[:, -1] = obs + with torch.no_grad(): + reference = module(obs, hist).numpy() + result = session.run(None, {"observation": obs.numpy(), "history": hist.numpy()})[0] + np.testing.assert_allclose(reference, result, rtol=1e-4, atol=1e-5) + maximum = max(maximum, float(np.max(np.abs(reference - result)))) + metadata = { + "format": "unilab-rma-deployment-v1", + "obs_dim": cfg.obs_dim, + "history_length": cfg.history_length, + "history_order": "oldest_to_newest_including_current", + "history_reset": "zero_pad_then_append_reset_observation", + "action_dim": cfg.action_dim, + "latent_dim": cfg.latent_dim, + "policy_period_s": 0.02, + "action_scale": 0.25, + "normalization": "raw UniLab policy observations; no running normalization", + "observation_fields": [ + ["gyro", 3], + ["projected_gravity", 3], + ["joint_pos_rel", 12], + ["joint_vel_rel", 12], + ["last_action", 12], + ["command", 3], + ["gait_phase", 4], + ], + "joint_order": ["FL", "FR", "RL", "RR"], + "action_actuator_order": ["FL", "FR", "RL", "RR"], + "action_actuator_order_note": "Legacy alias for action output order, NOT scene storage order", + **go2_control_layout(), + "per_leg_order": ["hip", "thigh", "calf"], + "onnx_max_abs_error": maximum, + "default_angles": "must read matching scene home keyframe in actuator order; not supplied by source-only copy", + } + args.output.with_suffix(".json").write_text(json.dumps(metadata, indent=2), encoding="utf-8") + print(f"ONNX verified: {args.output}; max_abs_error={maximum:.8g}") + + +if __name__ == "__main__": + main() diff --git a/scripts/inspect_rma_task.py b/scripts/inspect_rma_task.py new file mode 100644 index 000000000..30e5ebd3d --- /dev/null +++ b/scripts/inspect_rma_task.py @@ -0,0 +1,59 @@ +"""Show fully resolved task differences without building an environment.""" + +import argparse +import json + +from hydra import compose, initialize_config_dir +from omegaconf import OmegaConf +from train_rma import ROOT +from unilab_rma.evaluation_diagnostics import FORWARD_TASK, RMA_TASKS + + +def differences(before, after, prefix=""): + if isinstance(before, dict) and isinstance(after, dict): + result = [] + for key in sorted(before.keys() | after.keys()): + path = f"{prefix}.{key}" if prefix else key + if key not in before or key not in after: + result.append(dict(path=path, before=before.get(key), after=after.get(key))) + else: + result.extend(differences(before[key], after[key], path)) + return result + if before != after: + return [dict(path=prefix, before=before, after=after)] + return [] + + +def resolved(task): + with initialize_config_dir(config_dir=str(ROOT / "src/unilab/conf/ppo"), version_base=None): + config = compose(config_name="config", overrides=[f"task={task}"]) + return OmegaConf.to_container(config, resolve=True) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--task", choices=RMA_TASKS, required=True) + parser.add_argument("--compare-with", choices=RMA_TASKS, default=FORWARD_TASK) + parser.add_argument("--expect-only", help="Fail unless exactly this resolved field changes") + args = parser.parse_args() + baseline, candidate = resolved(args.compare_with), resolved(args.task) + changes = differences(baseline, candidate) + print( + json.dumps( + dict( + root=str(ROOT), + baseline=args.compare_with, + candidate=args.task, + baseline_tracking=baseline["reward"]["tracking_lin_vel"], + candidate_tracking=candidate["reward"]["tracking_lin_vel"], + changes=changes, + ), + indent=2, + ) + ) + if args.expect_only and [row["path"] for row in changes] != [args.expect_only]: + parser.exit(1, "Single-variable check FAILED; inspect changes above.\n") + + +if __name__ == "__main__": + main() diff --git a/scripts/plot_evaluation_trace.py b/scripts/plot_evaluation_trace.py new file mode 100644 index 000000000..f2b955b9b --- /dev/null +++ b/scripts/plot_evaluation_trace.py @@ -0,0 +1,123 @@ +"""Create quick-look plots from an opt-in RMA evaluation trace.""" + +import argparse +import csv +import gzip +import json +from collections import defaultdict +from pathlib import Path + + +def plot_trace(trace_dir, output_dir=None): + import matplotlib + + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + trace_dir = Path(trace_dir) + output_dir = Path(output_dir) if output_dir else trace_dir / "plots" + output_dir.mkdir(parents=True, exist_ok=True) + metadata_path = trace_dir / "run_config.json" + if not metadata_path.exists(): + metadata_path = trace_dir / "run_configuration.json" + metadata = json.loads(metadata_path.read_text(encoding="utf-8")) + csv_path = trace_dir / "transitions.csv" + if not csv_path.exists(): + csv_path = trace_dir / "transitions.csv.gz" + opener = gzip.open if csv_path.suffix == ".gz" else open + with opener(csv_path, "rt", newline="", encoding="utf-8") as stream: + rows = list(csv.DictReader(stream)) + if not rows: + raise ValueError(f"No transitions in {trace_dir / 'transitions.csv'}") + steps = sorted({int(row["step"]) for row in rows}) + velocity_by_step = defaultdict(list) + command_by_step = defaultdict(list) + action_rows = [] + reward_columns = [name for name in rows[0] if name.startswith("reward_rate/")] + reward_by_step = {name: defaultdict(list) for name in reward_columns} + for row in rows: + step = int(row["step"]) + velocity_by_step[step].append(float(row["before_vx_m_s"])) + command_by_step[step].append(float(row["command_vx_m_s"])) + action_rows.append([float(row[f"action_{i}"]) for i in range(12)]) + for name in reward_columns: + reward_by_step[name][step].append(float(row[name])) + + dt = float(metadata["control_period_s"]) + elapsed = [step * dt for step in steps] + velocity_mean = [sum(velocity_by_step[step]) / len(velocity_by_step[step]) for step in steps] + velocity_std = [ + (sum((value - mean) ** 2 for value in velocity_by_step[step]) / len(velocity_by_step[step])) + ** 0.5 + for step, mean in zip(steps, velocity_mean, strict=True) + ] + commands = [sum(command_by_step[step]) / len(command_by_step[step]) for step in steps] + fig, axis = plt.subplots(figsize=(10, 4), constrained_layout=True) + axis.plot(elapsed, commands, "--", color="#C53434", label="Command") + axis.plot(elapsed, velocity_mean, color="#1D4C79", label="Mean measured vx") + axis.fill_between( + elapsed, + [mean - std for mean, std in zip(velocity_mean, velocity_std, strict=True)], + [mean + std for mean, std in zip(velocity_mean, velocity_std, strict=True)], + color="#4679A8", + alpha=0.2, + label="Across-env standard deviation", + ) + axis.set( + xlabel="Time since evaluation start (s)", + ylabel="Body-frame vx (m/s)", + title="Command tracking; each point averages active environments, resets included", + ) + axis.grid(alpha=0.2) + axis.legend() + fig.savefig(output_dir / "velocity_tracking.png", dpi=160) + plt.close(fig) + + joint_names = metadata["control_contract"]["action_target_joint_order"] + fig, axis = plt.subplots(figsize=(12, 4), constrained_layout=True) + axis.boxplot( + [[row[index] for row in action_rows] for index in range(12)], + showfliers=False, + ) + axis.set_xticks(range(1, 13), joint_names) + axis.axhline(0.95, color="#C53434", linestyle="--", linewidth=1) + axis.axhline(-0.95, color="#C53434", linestyle="--", linewidth=1) + axis.set( + xlabel="Resolved joint target order", + ylabel="Normalized policy action", + title="Action distribution; dashed lines flag the |action| ≥ 0.95 indicator", + ) + axis.tick_params(axis="x", rotation=35) + axis.grid(axis="y", alpha=0.2) + fig.savefig(output_dir / "action_distribution.png", dpi=160) + plt.close(fig) + + if reward_columns: + fig, axis = plt.subplots(figsize=(11, 5), constrained_layout=True) + for name in reward_columns: + values = [ + sum(reward_by_step[name][step]) / len(reward_by_step[name][step]) for step in steps + ] + axis.plot(elapsed, values, label=name.removeprefix("reward_rate/")) + axis.set( + xlabel="Time since evaluation start (s)", + ylabel="Weighted reward rate before control-dt scaling", + title="Reward components averaged across active environments", + ) + axis.grid(alpha=0.2) + axis.legend(fontsize=8, ncol=2) + fig.savefig(output_dir / "reward_components.png", dpi=160) + plt.close(fig) + return sorted(str(path) for path in output_dir.glob("*.png")) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("trace_dir", type=Path) + parser.add_argument("--output", type=Path) + args = parser.parse_args() + print(json.dumps(plot_trace(args.trace_dir, args.output), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/report_rma.py b/scripts/report_rma.py new file mode 100644 index 000000000..1a1f6e950 --- /dev/null +++ b/scripts/report_rma.py @@ -0,0 +1,22 @@ +"""Rebuild the checked-in RMA result tables and figures.""" + +import argparse +import json +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "rsl_rl")) + +from unilab_rma.experiment_report import build_report + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--results", type=Path, default=ROOT / "results/rma") + args = parser.parse_args() + print(json.dumps(build_report(args.results), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/train_rma.py b/scripts/train_rma.py new file mode 100644 index 000000000..8514d153e --- /dev/null +++ b/scripts/train_rma.py @@ -0,0 +1,106 @@ +"""Run from project root; requires the original UniLab runtime and robot assets.""" + +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "src")) +sys.path.insert(0, str(ROOT / "rsl_rl")) +import argparse + +import torch +from unilab_rma import ActorCriticRMA, AdaptationModule, RMAConfig +from unilab_rma.env_adapter import UniLabRMAEnv +from unilab_rma.evaluation_diagnostics import DEFAULT_TASK, RMA_TASKS, command_overrides +from unilab_rma.runner import train_student, train_teacher + + +def build_environment(num_envs, cfg, device, seed=42, *, task=DEFAULT_TASK, command=None): + from hydra import compose, initialize_config_dir + from unilab_rma.scene_contract import prepare_scene + + from unilab.base.config_adapter import BackendAdapter, create_env + from unilab.base.registry import ensure_registries + + if task not in RMA_TASKS: + raise ValueError(f"Unsupported RMA task: {task}") + overrides = [f"task={task}"] + if command is not None: + overrides.extend(command_overrides(command)) + with initialize_config_dir(config_dir=str(ROOT / "src/unilab/conf/ppo"), version_base=None): + composed = compose(config_name="config", overrides=overrides) + ensure_registries() + adapter = BackendAdapter(composed, root_dir=ROOT, algo_name="ppo") + override = adapter.build_task_env_cfg_override() + prepare_scene(override, ROOT) + override["auto_reset"] = False + override["seed"] = seed + raw = create_env(composed, num_envs=num_envs, env_cfg_override=override) + try: + return raw, UniLabRMAEnv(raw, cfg, device) + except Exception: + raw.close() + raise + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--phase", choices=["teacher", "student"], required=True) + parser.add_argument("--checkpoint", type=Path) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--iterations", type=int, default=1000) + parser.add_argument("--num-envs", type=int, default=64) + parser.add_argument("--device", default="cpu") + parser.add_argument( + "--task", + choices=RMA_TASKS, + default=DEFAULT_TASK, + help="Original RMA task or an isolated forward-walking experiment", + ) + parser.add_argument( + "--log-dir", + type=Path, + help="Parent for unique CSV/TensorBoard runs; default: _logs", + ) + args = parser.parse_args() + if args.iterations < 1 or args.num_envs < 1: + parser.error("iterations and num-envs must be positive") + if args.phase == "student" and args.checkpoint is None: + parser.error("Student training requires a trained teacher checkpoint") + if args.output.exists(): + parser.error(f"Output already exists: {args.output}. Choose a new --output to keep it.") + torch.manual_seed(42) + cfg = RMAConfig() + model, adaptation = ActorCriticRMA(cfg).to(args.device), AdaptationModule(cfg).to(args.device) + if args.checkpoint: + saved = torch.load(args.checkpoint, map_location=args.device, weights_only=True) + if saved.get("format") != "unilab-rma-v1": + raise ValueError("Not an RMA checkpoint") + if saved["config"] != model.metadata(): + raise ValueError("Checkpoint dimension/config mismatch") + model.load_state_dict(saved["teacher"], strict=True) + adaptation.load_state_dict(saved["adaptation"], strict=True) + raw, env = build_environment(args.num_envs, cfg, args.device, task=args.task) + try: + fn = train_teacher if args.phase == "teacher" else train_student + from unilab_rma.training_log import TrainingLog + + metadata = dict( + arguments=vars(args), + model_config=model.metadata(), + horizon=24, + seed=42, + torch_version=torch.__version__, + requested_policy_device=args.device, + environment_device=str(raw.device), + sim_backend="mujoco", + ) + with TrainingLog(args.output, args.phase, metadata, args.log_dir) as logger: + print(f"Training logs: {logger.directory}", flush=True) + fn(env, model, adaptation, args.iterations, args.output, logger=logger) + finally: + raw.close() + + +if __name__ == "__main__": + main() diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/rma.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/rma.yaml new file mode 100644 index 000000000..24bc7c893 --- /dev/null +++ b/src/unilab/conf/ppo/task/go2_joystick_flat/rma.yaml @@ -0,0 +1,60 @@ +# @package _global_ +defaults: + - /task/go2_joystick_flat/mujoco + - _self_ + +# Existing non-RMA configs remain unchanged. train_rma.py composes this overlay. +env: + policy_observation_group: policy + critic_observation_group: critic + scene: + entities: + robot: + body_names: [FL_calf, base] + # train_rma.py fills exact named robot geoms from the restored XML assets. + geom_names: [] + observations: + privileged: + terms: + parameters: + func: unilab.envs.mdp.rma.privileged_parameters + events: + randomize_leg_mass: null + rma_base_load: + func: unilab.envs.mdp.rma.RMABaseMass + mode: reset + params: + asset_cfg: + _target_: unilab.managers.SceneEntityCfg + name: robot + body_names: [base] + operation: add + distribution: uniform + mass_distribution_params: [0.0, 5.0] + recompute_inertia: false + rma_friction: + func: unilab.envs.mdp.rma.RMAFriction + mode: reset + params: + asset_cfg: + _target_: unilab.managers.SceneEntityCfg + name: robot + geom_names: ['.*'] + ranges: [0.4, 1.25] + axes: [0] + shared_random: true + operation: abs + auto_reset: false + +algo: + obs_groups: + actor: [policy] + critic: [critic] + +rma: + obs_dim: 49 + critic_dim: 52 + privileged_dim: 2 + history_length: 50 + latent_dim: 8 + action_dim: 12 diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward.yaml new file mode 100644 index 000000000..26f3e85d4 --- /dev/null +++ b/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward.yaml @@ -0,0 +1,20 @@ +# @package _global_ +# Experimental forward-walking curriculum. Original rma task is unchanged. +# Same policy I/O, dynamics, action scale and randomization as the RMA baseline. +defaults: + - /task/go2_joystick_flat/rma + - _self_ + +env: + commands: + twist: + ranges: + lin_vel_x: [0.3, 0.6] + lin_vel_y: [0.0, 0.0] + ang_vel_z: [0.0, 0.0] + +reward: + tracking_lin_vel: + weight: 2.0 + swing_feet_z: + weight: 0.25 diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_no_pose.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_no_pose.yaml new file mode 100644 index 000000000..4072efc5d --- /dev/null +++ b/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_no_pose.yaml @@ -0,0 +1,9 @@ +# @package _global_ +# EXP-003: disable only the default-pose deviation penalty. +defaults: + - /task/go2_joystick_flat/rma_forward + - _self_ + +reward: + similar_to_default: + weight: 0.0 diff --git a/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_tight_tracking.yaml b/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_tight_tracking.yaml new file mode 100644 index 000000000..753025011 --- /dev/null +++ b/src/unilab/conf/ppo/task/go2_joystick_flat/rma_forward_tight_tracking.yaml @@ -0,0 +1,12 @@ +# @package _global_ +# Native 1.3.4 profile: denominator is tracking_sigma, NOT std squared. +# Effective width 0.35 m/s corresponds to 0.35**2 = 0.1225. +# The 1.3.3 experiment profile is in experiments/rma/runtime_1_3_3/. +defaults: + - /task/go2_joystick_flat/rma_forward + - _self_ + +reward: + tracking_lin_vel: + params: + tracking_sigma: 0.1225 diff --git a/src/unilab/envs/mdp/rma.py b/src/unilab/envs/mdp/rma.py new file mode 100644 index 000000000..bd225bf19 --- /dev/null +++ b/src/unilab/envs/mdp/rma.py @@ -0,0 +1,54 @@ +"""RMA reset labels recorded from the SAME samples written to simulation. + +Only manager reset-transaction backends are supported; no guessed fallback labels. +Friction is the selected robot geoms' sliding coefficient, not an effective +robot/terrain contact coefficient (contact mixing remains backend-specific). +""" + +import numpy as np +import torch + +from unilab.envs.mdp.events import GeomFriction, RandomizeRigidBodyMass, resolve_env_ids + + +def _labels(env): + if not hasattr(env, "_rma_privileged"): + # Initialization defaults only, overwritten by the reset events. + env._rma_privileged = np.tile(np.array([1.0, 0.0]), (env.num_envs, 1)) + env._rma_label_valid = np.zeros((env.num_envs, 2), dtype=bool) + return env._rma_privileged + + +def privileged_parameters(env): + return torch.as_tensor(_labels(env), device=env.device, dtype=torch.float32) + + +class RMAFriction(GeomFriction): + def _write(self, values, env_ids): + coefficients = values[..., 0] + if not np.allclose(coefficients, coefficients[:, :1]): + raise ValueError( + "RMA scalar friction requires shared_random=true across selected geoms" + ) + super()._write(values, env_ids) + _labels(self._env)[env_ids, 0] = coefficients[:, 0] + self._env._rma_label_valid[env_ids, 0] = True + + +class RMABaseMass(RandomizeRigidBodyMass): + def __call__(self, env, env_ids, **kwargs): + if self._operation != "add" or self._distribution != "uniform" or len(self._body_ids) != 1: + raise ValueError("RMA load requires one base body, uniform additive mass") + ids = resolve_env_ids(env, env_ids) + samples = env.rng.uniform(*self._distribution_params, size=(ids.size, 1)) + defaults = self._default_mass + if defaults.ndim == 2: + defaults = defaults[ids] + values = defaults + samples + if np.any(values < self._min_mass): + raise ValueError("Invalid randomized base mass") + self._entity.write_body_mass_to_sim( + values, body_ids=self._body_ids, env_ids=ids, term_name="randomize_rigid_body_mass" + ) + _labels(env)[ids, 1] = samples[:, 0] + env._rma_label_valid[ids, 1] = True diff --git a/tests/rma/test_ablation_config.py b/tests/rma/test_ablation_config.py new file mode 100644 index 000000000..419225c9c --- /dev/null +++ b/tests/rma/test_ablation_config.py @@ -0,0 +1,98 @@ +"""Protect the single-variable experiment and existing checkpoints.""" + +import contextlib +import io +import sys +import tempfile +import unittest +from pathlib import Path +from unittest.mock import patch + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "scripts")) +sys.path.insert(0, str(ROOT / "rsl_rl")) +import train_rma +from inspect_rma_task import differences, resolved +from unilab_rma.evaluation_diagnostics import ( + FORWARD_TASK, + NO_POSE_TASK, + RMA_TASKS, + TIGHT_TRACKING_TASK, +) + + +class AblationConfigTests(unittest.TestCase): + def test_legacy_133_overlay_changes_only_std(self): + import yaml + + path = ROOT / "experiments/rma/runtime_1_3_3/rma_forward_tight_tracking.yaml" + overlay = yaml.safe_load(path.read_text(encoding="utf-8")) + self.assertEqual(overlay["defaults"], ["/task/go2_joystick_flat/rma_forward", "_self_"]) + self.assertEqual(overlay["reward"]["tracking_lin_vel"], {"params": {"std": 0.35}}) + + def test_native_tight_tracking_changes_only_denominator(self): + baseline, experiment = resolved(FORWARD_TASK), resolved(TIGHT_TRACKING_TASK) + self.assertIn(TIGHT_TRACKING_TASK, RMA_TASKS) + self.assertEqual( + differences(baseline, experiment), + [dict(path="reward.tracking_lin_vel.params.tracking_sigma", before=0.5, after=0.1225)], + ) + self.assertNotIn("std", experiment["reward"]["tracking_lin_vel"]["params"]) + self.assertEqual(experiment["reward"]["similar_to_default"]["weight"], -0.1) + + def test_inspector_catches_extra_changes_and_is_read_only(self): + before = {"reward": {"width": 0.5, "weight": 2}, "other": [1, 2]} + after = {"reward": {"width": 0.35, "weight": 3}, "other": [1, 2]} + changes = differences(before, after) + self.assertEqual([row["path"] for row in changes], ["reward.weight", "reward.width"]) + self.assertEqual(before["reward"], {"width": 0.5, "weight": 2}) + + def test_composed_experiment_changes_exactly_one_weight(self): + from hydra import compose, initialize_config_dir + from omegaconf import OmegaConf + + configs = [] + for task in (FORWARD_TASK, NO_POSE_TASK): + with initialize_config_dir( + config_dir=str(ROOT / "src/unilab/conf/ppo"), version_base=None + ): + config = compose(config_name="config", overrides=[f"task={task}"]) + configs.append(OmegaConf.to_container(config, resolve=True)) + baseline, experiment = configs + self.assertIn(NO_POSE_TASK, RMA_TASKS) + self.assertEqual(baseline["reward"]["similar_to_default"]["weight"], -0.1) + self.assertEqual(experiment["reward"]["similar_to_default"]["weight"], 0.0) + experiment["reward"]["similar_to_default"]["weight"] = -0.1 + self.assertEqual(experiment, baseline) + + def test_existing_output_is_rejected_before_model_or_environment_creation(self): + with tempfile.TemporaryDirectory() as directory: + output = Path(directory) / "existing.pt" + output.write_bytes(b"existing checkpoint must be preserved") + argv = [ + "train_rma.py", + "--phase", + "teacher", + "--task", + NO_POSE_TASK, + "--output", + str(output), + ] + stderr = io.StringIO() + with ( + patch.object(sys, "argv", argv), + patch.object(train_rma, "ActorCriticRMA") as model, + patch.object(train_rma, "build_environment") as build, + contextlib.redirect_stderr(stderr), + self.assertRaises(SystemExit) as error, + ): + train_rma.main() + self.assertEqual(error.exception.code, 2) + self.assertIn("Output already exists", stderr.getvalue()) + self.assertEqual(output.read_bytes(), b"existing checkpoint must be preserved") + model.assert_not_called() + build.assert_not_called() + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/rma/test_control_diagnostics.py b/tests/rma/test_control_diagnostics.py new file mode 100644 index 000000000..c8e76db03 --- /dev/null +++ b/tests/rma/test_control_diagnostics.py @@ -0,0 +1,97 @@ +"""Position-control telemetry must resolve storage order and reset boundaries.""" + +import sys +import tempfile +import unittest +from pathlib import Path +from types import SimpleNamespace + +import numpy as np +import torch + +sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "rsl_rl")) +from unilab_rma.control_diagnostics import ControlDiagnostics + + +def fixture(): + names = [ + f"{leg}_{joint}_joint" + for leg in ("FL", "FR", "RL", "RR") + for joint in ("hip", "thigh", "calf") + ] + permutation = np.array([3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]) + target = torch.arange(12, dtype=torch.float32).reshape(1, 12) / 10 + bias = np.full((1, 12), 0.02) + buffer = np.zeros((1, 12)) + buffer[:, permutation] = target.numpy() - bias + actuators = [f"motor_{i}" for i in range(12)] + data = SimpleNamespace( + joint_pos=np.zeros((1, 12)), + joint_vel=np.ones((1, 12)), + encoder_bias=bias, + control_buffer=buffer, + ) + term = SimpleNamespace( + cfg=SimpleNamespace(entity_name="robot"), target_names=names, processed_action=target + ) + robot = SimpleNamespace(joint_names=names, actuator_names=actuators, data=data) + backend = SimpleNamespace( + get_actuator_names=lambda: actuators, + get_actuator_joint_names=lambda: [names[i] for i in permutation], + get_actuator_gains=lambda: (np.full(12, 35), np.full(12, 0.5)), + ) + raw = SimpleNamespace( + num_envs=1, + step_dt=0.02, + scene={"robot": robot}, + action_manager=SimpleNamespace(get_term=lambda _: term), + backend=backend, + ) + return raw, target, permutation + + +class ControlDiagnosticsTests(unittest.TestCase): + def test_permuted_targets_bias_and_before_snapshot(self): + raw, action, _ = fixture() + with tempfile.TemporaryDirectory() as directory: + recorder = ControlDiagnostics(raw, directory) + before = recorder.before() + raw.scene["robot"].data.joint_pos[:] = 0.2 + recorder.record(before, action, False, False) + np.testing.assert_array_equal(before[0], np.zeros(12)) + self.assertLess(max(abs(row["mapping_residual_rad"]) for row in recorder.rows), 1e-7) + self.assertAlmostEqual(recorder.rows[0]["pd_demand_estimate_nm"], -1.2) + self.assertAlmostEqual(recorder.rows[0]["q_after_rad"], 0.2) + torch.testing.assert_close( + raw.action_manager.get_term("joint_pos").processed_action, action + ) + + def test_direct_copy_bug_is_detected(self): + raw, action, _ = fixture() + raw.scene["robot"].data.control_buffer[:] = action.numpy() - 0.02 + with tempfile.TemporaryDirectory() as directory: + recorder = ControlDiagnostics(raw, directory) + recorder.record(recorder.before(), action, False, False) + self.assertGreater(max(abs(row["mapping_residual_rad"]) for row in recorder.rows), 0.2) + + def test_reset_boundary_has_new_episode_and_copied_terminal_state(self): + raw, action, _ = fixture() + with tempfile.TemporaryDirectory() as directory: + recorder = ControlDiagnostics(raw, directory) + recorder.record(recorder.before(), action, True, False) + raw.scene["robot"].data.joint_pos[:] = 9 + recorder.record(recorder.before(), action, False, False) + self.assertEqual(recorder.rows[0]["q_after_rad"], 0) + self.assertEqual(recorder.rows[12]["episode"], 1) + self.assertEqual(recorder.rows[12]["episode_step"], 1) + self.assertEqual(recorder.rows[12]["q_before_rad"], 9) + + def test_multiple_worlds_rejected(self): + raw, _, _ = fixture() + raw.num_envs = 2 + with tempfile.TemporaryDirectory() as directory, self.assertRaises(ValueError): + ControlDiagnostics(raw, directory) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/rma/test_core.py b/tests/rma/test_core.py new file mode 100644 index 000000000..f869cf864 --- /dev/null +++ b/tests/rma/test_core.py @@ -0,0 +1,207 @@ +import sys +import tempfile +import unittest +from pathlib import Path +from types import SimpleNamespace + +import numpy as np +import torch + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "rsl_rl")) +from unilab_rma import ActorCriticRMA, AdaptationModule, AdaptationTrainer, HistoryBuffer, RMAConfig +from unilab_rma.deployment import DeploymentPolicy, go2_control_layout +from unilab_rma.env_adapter import UniLabRMAEnv +from unilab_rma.ppo import gae +from unilab_rma.runner import train_student, train_teacher +from unilab_rma.scene_contract import expanded_xml, prepare_scene + +torch.set_num_threads(1) + + +class SyntheticEnv: + """Synthetic tensors ONLY, not a Go2 simulation or performance benchmark.""" + + num_envs = 2 + + def reset(self): + self.t = 0 + return self.obs() + + def obs(self): + return ( + torch.randn(2, 49) * 0.1, + torch.randn(2, 52) * 0.1, + torch.tensor([[0.5, 1.0], [1.0, 2.0]]), + ) + + def step(self, action): + self.t += 1 + terminal = self.obs() + done = torch.tensor([self.t % 3 == 0, self.t % 4 == 0]) + return ( + self.obs(), + -action.square().mean(-1), + done, + torch.zeros(2, dtype=torch.bool), + terminal, + ) + + +class CoreTests(unittest.TestCase): + def setUp(self): + torch.manual_seed(4) + self.cfg = RMAConfig() + + def test_shapes_and_log_probability(self): + m = ActorCriticRMA(self.cfg) + a, raw, logp = m.sample(torch.zeros(3, 49), torch.ones(3, 2)) + self.assertEqual(a.shape, (3, 12)) + self.assertTrue(torch.isfinite(logp).all()) + self.assertTrue((a.abs() <= 1).all()) + self.assertTrue( + torch.isfinite( + m.log_prob( + m.distribution(torch.zeros(3, 49), torch.ones(3, 2)), + torch.full_like(raw, 100.0), + ) + ).all() + ) + + def test_history_reset(self): + h = HistoryBuffer(2, self.cfg) + h.push(torch.ones(2, 49)) + h.push(torch.full((2, 49), 2.0), torch.tensor([True, False])) + self.assertEqual(h.data[0, :-1].abs().sum().item(), 0) + self.assertTrue((h.data[1, -2] == 1).all()) + self.assertTrue((h.data[:, -1] == 2).all()) + + def test_timeout_and_termination(self): + r = torch.tensor([[1.0, 1.0], [100.0, 100.0]]) + v = torch.zeros_like(r) + nxt = torch.full_like(r, 10.0) + term = torch.tensor([[True, False], [False, False]]) + trunc = torch.tensor([[False, True], [False, False]]) + a, _ = gae(r, v, nxt, term, trunc) + self.assertAlmostEqual(a[0, 0].item(), 1.0) + self.assertAlmostEqual(a[0, 1].item(), 10.9, places=5) + + def test_supervision_freezes_teacher(self): + teacher, student = ActorCriticRMA(self.cfg), AdaptationModule(self.cfg) + saved = {k: v.clone() for k, v in teacher.state_dict().items()} + trainer = AdaptationTrainer(teacher, student, capacity=8) + history = torch.randn(2, 50, 49) + trainer.add(history, torch.ones(2, 2)) + with torch.no_grad(): + initial = (student(history) - teacher.encoder(torch.ones(2, 2))).square().mean().item() + for _ in range(50): + trainer.update(batch_size=2, updates=2) + with torch.no_grad(): + final = (student(history) - teacher.encoder(torch.ones(2, 2))).square().mean().item() + self.assertLess(final, initial) + for key, value in teacher.state_dict().items(): + self.assertTrue(torch.equal(value, saved[key])) + self.assertTrue(all(p.grad is None for p in teacher.parameters())) + + def test_rollout_update_and_checkpoint(self): + teacher, student = ActorCriticRMA(self.cfg), AdaptationModule(self.cfg) + before = teacher.actor.net[0].weight.detach().clone() + with tempfile.TemporaryDirectory() as d: + p = Path(d) / "teacher.pt" + train_teacher(SyntheticEnv(), teacher, student, 1, p, horizon=4) + self.assertFalse(torch.equal(before, teacher.actor.net[0].weight)) + train_student(SyntheticEnv(), teacher, student, 1, Path(d) / "student.pt", horizon=4) + saved = torch.load(Path(d) / "student.pt", weights_only=True) + self.assertEqual(saved["phase"], "student") + deploy = DeploymentPolicy(self.cfg) + deploy.actor.load_state_dict(teacher.actor.state_dict()) + deploy.adaptation.load_state_dict(student.state_dict()) + obs, h = torch.randn(2, 49), torch.randn(2, 50, 49) + torch.testing.assert_close(deploy(obs, h), teacher.actor(obs, student(h)).tanh()) + self.assertFalse(any("encoder" in k or "critic" in k for k in deploy.state_dict())) + + def test_scene_names_from_xml_includes(self): + with tempfile.TemporaryDirectory() as d: + p = Path(d) + (p / "robot.xml").write_text( + '' + ) + (p / "scene.xml").write_text( + '' + ) + override = {"scene": {"model_file": "scene.xml", "entities": {"robot": {}}}} + prepare_scene(override, p) + self.assertEqual(override["scene"]["entities"]["robot"]["geom_names"], ["foot"]) + + def test_export_control_layout_matches_current_task_names(self): + from hydra import compose, initialize_config_dir + + with initialize_config_dir(config_dir=str(ROOT / "src/unilab/conf/ppo"), version_base=None): + config = compose(config_name="config", overrides=["task=go2_joystick_flat/rma"]) + robot = config.env.scene.entities.robot + layout = go2_control_layout() + joint_names = list(robot.joint_names) + actuators = list(robot.actuator_names) + self.assertEqual( + layout["action_to_actuator_indices"], + [actuators.index(name.removesuffix("_joint")) for name in joint_names], + ) + self.assertEqual(layout["action_joint_order"], ["FL", "FR", "RL", "RR"]) + self.assertEqual(layout["scene_actuator_order"], ["FR", "FL", "RR", "RL"]) + + def test_adapter_preserves_pre_reset_state(self): + class Raw: + _autoreset = False + num_envs = 2 + device = "cpu" + _rma_label_valid = np.ones((2, 2), dtype=bool) + + def __init__(self): + self.data = { + k: torch.zeros(2, n) + for k, n in [("policy", 49), ("critic", 52), ("privileged", 2)] + } + + def step(self, action): + for x in self.data.values(): + x.fill_(7.0) + return SimpleNamespace( + obs=self.data, + reward=torch.ones(2), + terminated=torch.tensor([False, False]), + truncated=torch.tensor([True, False]), + ) + + def reset(self, ids): + for x in self.data.values(): + x[ids] = 0 + return self.data, {} + + def get_observations(self): + return self.data + + env = UniLabRMAEnv(Raw(), self.cfg) + current, _, term, trunc, terminal = env.step(torch.zeros(2, 12)) + self.assertEqual(terminal[0][0, 0].item(), 7.0) + self.assertEqual(current[0][0, 0].item(), 0.0) + self.assertEqual(current[0][1, 0].item(), 7.0) + + def test_adapter_public_mapping_and_label_snapshot(self): + raw = SimpleNamespace( + _autoreset=False, + num_envs=2, + _rma_label_valid=np.ones((2, 2), dtype=bool), + _rma_privileged=np.array([[0.5, 1.0], [1.0, 2.0]]), + ) + env = UniLabRMAEnv(raw, self.cfg) + obs, critic, priv = env.read({"obs": torch.zeros(2, 49), "critic": torch.ones(2, 52)}) + self.assertEqual(obs.shape, (2, 49)) + self.assertEqual(critic.shape, (2, 52)) + raw._rma_privileged[0] = 0 + self.assertEqual(priv[0, 1].item(), 1.0) + with self.assertRaisesRegex(KeyError, "critic_observation_group"): + env.read({"obs": torch.zeros(2, 49)}) + + +if __name__ == "__main__": + unittest.main(verbosity=2) diff --git a/tests/rma/test_evaluation_diagnostics.py b/tests/rma/test_evaluation_diagnostics.py new file mode 100644 index 000000000..1031ddff3 --- /dev/null +++ b/tests/rma/test_evaluation_diagnostics.py @@ -0,0 +1,94 @@ +"""Command/reset contract and diagnostic arithmetic, independent of assets.""" + +import sys +import unittest +from pathlib import Path +from types import SimpleNamespace + +import torch + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "rsl_rl")) +from unilab_rma.evaluation_diagnostics import ( + DEFAULT_TASK, + FORWARD_TASK, + EvaluationDiagnostics, + command_overrides, +) + + +class EvaluationDiagnosticTests(unittest.TestCase): + def test_fixed_command_is_a_config_override_not_a_one_time_tensor_patch(self): + overrides = command_overrides((0.5, 0, 0)) + self.assertIn("env.commands.twist.ranges.lin_vel_x=[0.5,0.5]", overrides) + self.assertIn("env.commands.twist.ranges.ang_vel_z=[0.0,0.0]", overrides) + self.assertIn("env.commands.twist.rel_standing_envs=0.0", overrides) + with self.assertRaises(ValueError): + command_overrides((float("nan"), 0, 0)) + with self.assertRaises(ValueError): + command_overrides((0, 0)) + + def test_clean_body_velocity_and_reward_units(self): + diagnostics = EvaluationDiagnostics() + critic = torch.zeros(2, 52) + critic[:, 42] = 0.5 + critic[:, 49] = 0.1 + critic[:, 2] = 0.2 + original = critic.clone() + diagnostics.record_state(critic) + manager = SimpleNamespace( + step_reward_names=("tracking_lin_vel", "base_height"), + step_reward_means=torch.tensor([0.4, -0.1]), + ) + diagnostics.record_rewards(manager) + manager.step_reward_means = torch.tensor([0.6, -0.3]) + diagnostics.record_rewards(manager) + report = diagnostics.report() + self.assertEqual(report["mean_command"], [0.5, 0, 0]) + self.assertAlmostEqual(report["mean_measured_velocity"][0], 0.1, places=6) + self.assertAlmostEqual(report["mean_measured_velocity"][2], 0.2, places=6) + self.assertAlmostEqual(report["tracking_rmse_by_axis"][0], 0.4, places=6) + self.assertAlmostEqual(report["tracking_rmse_by_axis"][2], 0.2, places=6) + terms = report["weighted_reward_terms_before_dt"] + self.assertAlmostEqual(terms["tracking_lin_vel"], 0.5, places=6) + self.assertAlmostEqual(terms["base_height"], -0.2, places=6) + self.assertTrue(torch.equal(critic, original)) + + def test_empty_report_and_invalid_contract(self): + diagnostics = EvaluationDiagnostics() + self.assertIsNone(diagnostics.report()["mean_command"]) + with self.assertRaises(ValueError): + diagnostics.record_state(torch.zeros(1, 49)) + + def test_forward_task_keeps_policy_contract_and_changes_only_experiment_drivers(self): + from hydra import compose, initialize_config_dir + from omegaconf import OmegaConf + + configs = [] + for task in (DEFAULT_TASK, FORWARD_TASK): + with initialize_config_dir( + config_dir=str(ROOT / "src/unilab/conf/ppo"), version_base=None + ): + configs.append(compose(config_name="config", overrides=[f"task={task}"])) + original, forward = configs + self.assertEqual( + OmegaConf.to_container(original.env.observations), + OmegaConf.to_container(forward.env.observations), + ) + self.assertEqual( + OmegaConf.to_container(original.env.actions), + OmegaConf.to_container(forward.env.actions), + ) + self.assertEqual( + OmegaConf.to_container(original.env.events), OmegaConf.to_container(forward.env.events) + ) + self.assertEqual(list(forward.env.commands.twist.ranges.lin_vel_x), [0.3, 0.6]) + self.assertEqual(list(forward.env.commands.twist.ranges.ang_vel_z), [0.0, 0.0]) + self.assertEqual(original.reward.tracking_lin_vel.weight, 1.0) + self.assertEqual(forward.reward.tracking_lin_vel.weight, 2.0) + self.assertEqual(original.reward.swing_feet_z.weight, 4.0) + self.assertEqual(forward.reward.swing_feet_z.weight, 0.25) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/rma/test_evaluation_trace.py b/tests/rma/test_evaluation_trace.py new file mode 100644 index 000000000..b9061caa1 --- /dev/null +++ b/tests/rma/test_evaluation_trace.py @@ -0,0 +1,117 @@ +"""Trace alignment, reset boundaries and disabled-path behavior.""" + +import csv +import json +import sys +import tempfile +import unittest +from pathlib import Path +from types import SimpleNamespace + +import torch + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "rsl_rl")) +from unilab_rma.evaluation_trace import EvaluationTrace + + +def trace_raw(num_envs=2): + joint_names = [ + f"{leg}_{joint}_joint" + for leg in ("FL", "FR", "RL", "RR") + for joint in ("hip", "thigh", "calf") + ] + ids = [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8] + action = SimpleNamespace( + cfg=SimpleNamespace(entity_name="robot", clip=None), + target_names=[joint_names[i] for i in ids], + target_ids=ids, + scale=0.25, + offset=torch.zeros(num_envs, 12), + ) + rewards = SimpleNamespace( + step_reward_names=("tracking_lin_vel", "penalty"), + get_active_iterable_terms=lambda i: [ + ("tracking_lin_vel", [float(i + 1)]), + ("penalty", [-0.2]), + ], + ) + return SimpleNamespace( + step_dt=0.02, + reward_manager=rewards, + action_manager=SimpleNamespace(get_term=lambda name: action), + scene={"robot": SimpleNamespace(joint_names=joint_names)}, + ) + + +class EvaluationTraceTests(unittest.TestCase): + def test_terminal_state_reset_ids_and_units_are_not_mixed(self): + with tempfile.TemporaryDirectory() as directory: + checkpoint = Path(directory) / "weights.pt" + checkpoint.write_bytes(b"test checkpoint") + raw = trace_raw() + trace = EvaluationTrace(directory, raw, {"num_envs": 2}, checkpoint, warmup_steps=1) + before = torch.zeros(2, 52) + before[:, 42] = 0.5 + before[:, 49] = 0.1 + after = before.clone() + after[:, 49] = 0.25 + action = torch.zeros(2, 12) + action[:, 0] = 0.96 + original = before.clone(), after.clone(), action.clone() + trace.record( + before, + action, + after, + torch.tensor([0.016, 0.036]), + torch.tensor([True, False]), + torch.zeros(2, dtype=torch.bool), + raw.reward_manager, + ) + trace.record( + before, + action, + after, + torch.tensor([0.016, 0.036]), + torch.zeros(2, dtype=torch.bool), + torch.zeros(2, dtype=torch.bool), + raw.reward_manager, + ) + report = trace.finish({"total_transitions": 4}) + trace.close() + with (trace.directory / "transitions.csv").open() as stream: + rows = list(csv.DictReader(stream)) + metadata = json.loads((trace.directory / "run_config.json").read_text()) + self.assertEqual(len(rows), 4) + self.assertAlmostEqual(float(rows[0]["before_vx_m_s"]), 0.1) + self.assertAlmostEqual(float(rows[0]["after_vx_m_s"]), 0.25) + self.assertEqual(rows[2]["episode_id"], "1") + self.assertEqual(rows[2]["episode_step"], "1") + self.assertEqual(rows[3]["episode_id"], "0") + self.assertEqual(rows[3]["episode_step"], "2") + self.assertAlmostEqual(float(rows[1]["reward_rate/tracking_lin_vel"]), 2) + self.assertEqual(report["trace"]["full"]["transitions"], 4) + self.assertEqual(report["trace"]["after_startup_window"]["transitions"], 1) + self.assertEqual(report["trace"]["normalized_action_near_bound_fraction"][0], 1) + self.assertEqual( + metadata["control_contract"]["action_target_joint_order"][0], "FR_hip_joint" + ) + self.assertEqual( + metadata["control_contract"]["joint_observation_order"][0], "FL_hip_joint" + ) + for left, right in zip(original, (before, after, action), strict=True): + self.assertTrue(torch.equal(left, right)) + + def test_repeated_invocations_do_not_overwrite_and_empty_window_is_null(self): + with tempfile.TemporaryDirectory() as directory: + checkpoint = Path(directory) / "weights.pt" + checkpoint.write_bytes(b"checkpoint") + first = EvaluationTrace(directory, trace_raw(), {"num_envs": 2}, checkpoint) + second = EvaluationTrace(directory, trace_raw(), {"num_envs": 2}, checkpoint) + try: + self.assertNotEqual(first.directory, second.directory) + self.assertIsNone(first.report()["after_startup_window"]["tracking_rmse_by_axis"]) + self.assertIsNone(first.report()["normalized_action_min"]) + finally: + first.close() + second.close() diff --git a/tests/rma/test_experiment_report.py b/tests/rma/test_experiment_report.py new file mode 100644 index 000000000..7548d8b12 --- /dev/null +++ b/tests/rma/test_experiment_report.py @@ -0,0 +1,36 @@ +import json +import sys +import tempfile +import unittest +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "rsl_rl")) + +from unilab_rma.experiment_report import load_evaluations, rolling_mean + + +class ExperimentReportTests(unittest.TestCase): + def test_published_reports_have_same_protocol(self): + rows = load_evaluations(ROOT / "results/rma") + self.assertEqual(len(rows), 6) + self.assertEqual({row["seed"] for row in rows}, {123, 456, 789}) + self.assertTrue(all(row["total_transitions"] == 8000 for row in rows)) + self.assertTrue(all(row["vx_rmse_m_s"] > 0.48 for row in rows)) + + def test_trailing_mean_retains_startup(self): + self.assertEqual(rolling_mean([1.0, 3.0, 5.0], window=2), [1.0, 2.0, 4.0]) + + def test_rejects_student_report_in_teacher_comparison(self): + report = json.loads((ROOT / "results/rma/baseline/seed123.json").read_text()) + report["policy"] = "student" + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "baseline").mkdir() + (root / "baseline/seed123.json").write_text(json.dumps(report)) + with self.assertRaisesRegex(ValueError, "Unexpected evaluation protocol"): + load_evaluations(root) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/rma/test_plot_evaluation_trace.py b/tests/rma/test_plot_evaluation_trace.py new file mode 100644 index 000000000..8b78fb10b --- /dev/null +++ b/tests/rma/test_plot_evaluation_trace.py @@ -0,0 +1,49 @@ +import csv +import json +import sys +import tempfile +import unittest +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "scripts")) +from plot_evaluation_trace import plot_trace + + +class PlotEvaluationTraceTests(unittest.TestCase): + def test_generates_command_action_and_reward_figures(self): + with tempfile.TemporaryDirectory() as directory: + trace = Path(directory) / "eval_sample" + trace.mkdir() + (trace / "run_config.json").write_text( + json.dumps( + { + "control_period_s": 0.02, + "control_contract": { + "action_target_joint_order": [f"joint_{i}" for i in range(12)] + }, + } + ) + ) + fields = ["step", "before_vx_m_s", "command_vx_m_s", "reward_rate/track"] + fields += [f"action_{i}" for i in range(12)] + with (trace / "transitions.csv").open("w", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + for step in range(2): + writer.writerow( + { + "step": step, + "before_vx_m_s": 0.1 * step, + "command_vx_m_s": 0.5, + "reward_rate/track": 1.0, + **{f"action_{i}": 0.01 * i for i in range(12)}, + } + ) + output = plot_trace(trace) + self.assertEqual(len(output), 3) + self.assertTrue(all(Path(path).stat().st_size > 1000 for path in output)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/rma/test_rendering.py b/tests/rma/test_rendering.py new file mode 100644 index 000000000..ff41ad69a --- /dev/null +++ b/tests/rma/test_rendering.py @@ -0,0 +1,349 @@ +"""Rendering isolation/cleanup regressions; synthetic states, not Go2 benchmarks.""" + +import contextlib +import io +import json +import sys +import tempfile +import unittest +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import patch + +import torch + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "scripts")) +import evaluate_rma +from unilab_rma import ActorCriticRMA, AdaptationModule, RMAConfig + +try: + import mujoco +except ImportError: + mujoco = None + + +class SyntheticEnv: + def __init__(self): + self.actions = [] + + def observations(self): + return torch.randn(1, 49), torch.randn(1, 52), torch.ones(1, 2) + + def reset(self): + return self.observations() + + def step(self, action): + self.actions.append(action.clone()) + obs = self.observations() + done = torch.zeros(1, dtype=torch.bool) + return obs, -action.square().mean(-1), done, done, obs + + +class PlaybackEnv: + step_dt = 0.02 + play_capabilities = SimpleNamespace(supports_physics_state_playback=True) + + def __init__(self): + self.closed = False + if mujoco is not None: + self.model = mujoco.MjModel.from_xml_string( + '' + '' + ) + + def close(self): + self.closed = True + + def get_playback_model(self, env_index=0): + return self.model + + def get_physics_state_layout(self): + return SimpleNamespace( + nq=1, + nv=1, + nmocap=0, + split_state=lambda state: SimpleNamespace( + time=state[0], qpos=state[1:2], qvel=state[2:3] + ), + ) + + def get_physics_state_snapshot(self): + import numpy as np + + return np.array([[0.0, 0.2, 0.1]]) + + +class FakeViewer: + def __init__(self, open_checks=None): + self.cam = SimpleNamespace() + self.open_checks = open_checks + self.closed = False + self.frames = 0 + + def __enter__(self): + return self + + def __exit__(self, *args): + self.closed = True + + def lock(self): + return contextlib.nullcontext() + + def is_running(self): + if self.open_checks is None: + return True + result = self.open_checks > 0 + self.open_checks -= 1 + return result + + def sync(self): + self.frames += 1 + + +class EvaluationRenderingTests(unittest.TestCase): + @classmethod + def setUpClass(cls): + torch.set_num_threads(1) + cls.directory = tempfile.TemporaryDirectory() + cls.checkpoint = Path(cls.directory.name) / "student.pt" + cfg = RMAConfig() + teacher, adaptation = ActorCriticRMA(cfg), AdaptationModule(cfg) + torch.save( + { + "format": "unilab-rma-v1", + "phase": "student", + "config": teacher.metadata(), + "teacher": teacher.state_dict(), + "adaptation": adaptation.state_dict(), + }, + cls.checkpoint, + ) + + @classmethod + def tearDownClass(cls): + cls.directory.cleanup() + + def evaluate(self, raw, env, *extra): + output = io.StringIO() + argv = [ + "evaluate_rma.py", + "--checkpoint", + str(self.checkpoint), + "--num-envs", + "1", + "--steps", + "3", + *extra, + ] + with ( + patch.object(sys, "argv", argv), + patch.object(evaluate_rma, "build_environment", return_value=(raw, env)), + contextlib.redirect_stdout(output), + ): + evaluate_rma.main() + return json.loads(output.getvalue()) + + def test_default_does_not_import_rendering_and_keeps_transition_count(self): + raw, env = PlaybackEnv(), SyntheticEnv() + with patch.dict( + sys.modules, + { + "unilab_rma.rendering": None, + "unilab_rma.tensorrt_runtime": None, + "unilab_rma.evaluation_trace": None, + }, + ): + metrics = self.evaluate(raw, env) + self.assertEqual(metrics["total_transitions"], 3) + self.assertTrue(raw.closed) + + def test_diagnostics_preserve_default_metrics_and_actions(self): + raw, env = PlaybackEnv(), SyntheticEnv() + expected = self.evaluate(raw, env) + raw2, env2 = PlaybackEnv(), SyntheticEnv() + raw2.reward_manager = SimpleNamespace( + step_reward_names=("tracking_lin_vel",), step_reward_means=torch.tensor([0.5]) + ) + actual = self.evaluate(raw2, env2, "--diagnostics") + diagnostics = actual.pop("diagnostics") + self.assertEqual(actual, expected) + self.assertAlmostEqual( + diagnostics["weighted_reward_terms_before_dt"]["tracking_lin_vel"], 0.5 + ) + for left, right in zip(env.actions, env2.actions): + self.assertTrue(torch.equal(left, right)) + + def test_fixed_command_and_task_are_forwarded_to_environment_builder(self): + raw, env = PlaybackEnv(), SyntheticEnv() + argv = [ + "evaluate_rma.py", + "--checkpoint", + str(self.checkpoint), + "--num-envs", + "1", + "--steps", + "3", + "--command", + "0.5", + "0", + "0", + "--task", + "go2_joystick_flat/rma_forward", + ] + with ( + patch.object(sys, "argv", argv), + patch.object(evaluate_rma, "build_environment", return_value=(raw, env)) as build, + contextlib.redirect_stdout(io.StringIO()), + ): + evaluate_rma.main() + self.assertEqual( + build.call_args.kwargs, + {"task": "go2_joystick_flat/rma_forward", "command": [0.5, 0.0, 0.0]}, + ) + + def test_trace_preserves_actions_and_metrics_and_writes_separate_json(self): + raw, env = PlaybackEnv(), SyntheticEnv() + expected = self.evaluate(raw, env) + raw2, env2 = PlaybackEnv(), SyntheticEnv() + names = [f"joint_{i}" for i in range(12)] + term = SimpleNamespace( + cfg=SimpleNamespace(entity_name="robot", clip=None), + target_names=names, + target_ids=list(range(12)), + scale=0.25, + offset=torch.zeros(1, 12), + ) + raw2.action_manager = SimpleNamespace(get_term=lambda name: term) + raw2.scene = {"robot": SimpleNamespace(joint_names=names)} + raw2.reward_manager = SimpleNamespace( + step_reward_names=("tracking_lin_vel",), + get_active_iterable_terms=lambda index: [("tracking_lin_vel", [0.5])], + ) + with tempfile.TemporaryDirectory() as directory: + actual = self.evaluate(raw2, env2, "--trace-dir", directory) + trace = actual.pop("trace") + self.assertEqual(actual, expected) + self.assertEqual(trace["full"]["transitions"], 3) + saved = json.loads((Path(trace["directory"]) / "summary.json").read_text()) + self.assertEqual(saved["total_transitions"], 3) + self.assertTrue(raw2.closed) + for left, right in zip(env.actions, env2.actions, strict=True): + self.assertTrue(torch.equal(left, right)) + + def test_reset_failure_still_closes_environment(self): + raw, env = PlaybackEnv(), SyntheticEnv() + with patch.object(env, "reset", side_effect=RuntimeError("reset failed")): + with self.assertRaisesRegex(RuntimeError, "reset failed"): + self.evaluate(raw, env, "--render") + self.assertTrue(raw.closed) + + def test_tensorrt_actions_drive_environment_and_runtime_is_closed(self): + raw, env = PlaybackEnv(), SyntheticEnv() + + class Runtime: + closed = False + + def infer(self, obs, history): + self.history_matches_current = torch.equal(history[:, -1, :], obs) + return torch.full((1, 12), 0.125) + + def close(self): + self.closed = True + + runtime = Runtime() + module = SimpleNamespace(TensorRTWorker=lambda *args: runtime) + with patch.dict(sys.modules, {"unilab_rma.tensorrt_runtime": module}): + metrics = self.evaluate(raw, env, "--backend", "tensorrt", "--engine", "unused.engine") + self.assertTrue(runtime.closed and raw.closed and runtime.history_matches_current) + self.assertEqual(metrics["inference_backend"], "tensorrt_cpp") + self.assertEqual(metrics["total_transitions"], 3) + self.assertTrue( + all(torch.equal(action, torch.full((1, 12), 0.125)) for action in env.actions) + ) + + def test_tensorrt_failure_closes_runtime_and_environment(self): + raw, env = PlaybackEnv(), SyntheticEnv() + closed = [] + + def fail(*args): + raise RuntimeError("worker failed") + + runtime = SimpleNamespace(infer=fail, close=lambda: closed.append(True)) + module = SimpleNamespace(TensorRTWorker=lambda *args: runtime) + with patch.dict(sys.modules, {"unilab_rma.tensorrt_runtime": module}): + with self.assertRaisesRegex(RuntimeError, "worker failed"): + self.evaluate(raw, env, "--backend", "tensorrt", "--engine", "unused.engine") + self.assertEqual(closed, [True]) + self.assertTrue(raw.closed) + + @unittest.skipIf(mujoco is None, "MuJoCo is required for playback contract tests") + def test_render_keeps_actions_and_metrics_and_uses_separate_model(self): + import mujoco.viewer + + raw, env = PlaybackEnv(), SyntheticEnv() + expected = self.evaluate(raw, env) + raw2, env2, viewer = PlaybackEnv(), SyntheticEnv(), FakeViewer() + + def launch(model, data): + self.assertIsNot(model, raw2.model) + model.opt.gravity[:] = 0 # A viewer edit must not change physics. + return viewer + + with ( + patch("glfw.init", return_value=True), + patch("glfw.terminate"), + patch("mujoco.viewer.launch_passive", side_effect=launch), + patch("unilab_rma.rendering.time.perf_counter", return_value=0), + patch("unilab_rma.rendering.time.sleep") as sleep, + ): + actual = self.evaluate(raw2, env2, "--render", "--render-speed", "0.5") + self.assertEqual(actual, expected) + for a, b in zip(env.actions, env2.actions): + self.assertTrue(torch.equal(a, b)) + self.assertNotEqual(float(raw2.model.opt.gravity[2]), 0) + self.assertEqual(viewer.frames, 4) + self.assertTrue(viewer.closed and raw2.closed) + self.assertEqual(sleep.call_count, 3) + self.assertAlmostEqual(sleep.call_args.args[0], 0.04) + + @unittest.skipIf(mujoco is None, "MuJoCo is required for playback contract tests") + def test_window_close_reports_actual_transition_count(self): + import mujoco.viewer + + raw, env, viewer = PlaybackEnv(), SyntheticEnv(), FakeViewer(open_checks=1) + with ( + patch("glfw.init", return_value=True), + patch("glfw.terminate"), + patch("mujoco.viewer.launch_passive", return_value=viewer), + ): + metrics = self.evaluate(raw, env, "--render") + self.assertEqual(metrics["total_transitions"], 1) + self.assertTrue(viewer.closed and raw.closed) + + @unittest.skipIf(mujoco is None, "MuJoCo is required for playback contract tests") + def test_inference_failure_closes_viewer(self): + import mujoco.viewer + + raw, env, viewer = PlaybackEnv(), SyntheticEnv(), FakeViewer() + with ( + patch("glfw.init", return_value=True), + patch("glfw.terminate"), + patch("mujoco.viewer.launch_passive", return_value=viewer), + patch.object(env, "step", side_effect=RuntimeError("step failed")), + ): + with self.assertRaisesRegex(RuntimeError, "step failed"): + self.evaluate(raw, env, "--render") + self.assertTrue(viewer.closed and raw.closed) + + @unittest.skipIf(mujoco is None, "MuJoCo is required for playback contract tests") + def test_unavailable_display_fails_cleanly(self): + raw, env = PlaybackEnv(), SyntheticEnv() + with patch("glfw.init", return_value=False): + with self.assertRaisesRegex(RuntimeError, "no usable GLFW display"): + self.evaluate(raw, env, "--render") + self.assertTrue(raw.closed) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/rma/test_tensorrt_runtime.py b/tests/rma/test_tensorrt_runtime.py new file mode 100644 index 000000000..02c033b14 --- /dev/null +++ b/tests/rma/test_tensorrt_runtime.py @@ -0,0 +1,120 @@ +"""Bridge protocol and cleanup tests using an engine-independent subprocess.""" + +import os +import sys +import tempfile +import unittest +from pathlib import Path +from types import SimpleNamespace + +import torch + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "rsl_rl")) +from unilab_rma.tensorrt_runtime import TensorRTWorker + +FAKE_WORKER = """ +from pathlib import Path +import struct, sys, time +mode = Path(sys.argv[2]).read_text() +if mode == 'startup_failure': + raise SystemExit(2) +magic = b'BAD!' if mode == 'bad_header' else b'RMA1' +sys.stdout.buffer.write(struct.pack('<4sIII',magic,49,50,12)) +sys.stdout.buffer.flush() +if mode == 'bad_header': + raise SystemExit(0) +while True: + frame = sys.stdin.buffer.read((49+2450)*4) + if not frame: + break + if len(frame) != (49+2450)*4: + raise SystemExit(3) + values = struct.unpack('<2499f',frame) + action = [values[i]+values[49+2450-49+i] for i in range(12)] + if mode == 'stall': + time.sleep(60) + if mode == 'nan': + action[0] = float('nan') + encoded = struct.pack('<12f',*action) + if mode == 'short_output': + sys.stdout.buffer.write(encoded[:4]) + sys.stdout.buffer.flush() + break + sys.stdout.buffer.write(encoded) + sys.stdout.buffer.flush() +""" + + +@unittest.skipUnless(os.name == "posix", "The C++ bridge targets Linux/WSL pipes") +class TensorRTWorkerTests(unittest.TestCase): + def setUp(self): + self.directory = tempfile.TemporaryDirectory() + self.addCleanup(self.directory.cleanup) + self.folder = Path(self.directory.name) + self.worker = self.folder / "fake_worker" + self.worker.write_text("#!" + sys.executable + "\n" + FAKE_WORKER) + self.worker.chmod(0o755) + self.engine = self.folder / "fake.engine" + self.cfg = SimpleNamespace(obs_dim=49, history_length=50, action_dim=12) + + def start(self, mode="echo", **kwargs): + self.engine.write_text(mode) + runtime = TensorRTWorker(self.worker, self.engine, self.cfg, **kwargs) + self.addCleanup(runtime.close) + return runtime + + def test_multiple_environments_preserve_observation_and_history_order(self): + runtime = self.start() + process = runtime._process + obs = torch.arange(98, dtype=torch.float32).reshape(2, 49) + history = torch.zeros(2, 50, 49) + history[:, -1, :] = -3 * obs + actual = runtime.infer(obs, history) + self.assertTrue(torch.equal(actual, -2 * obs[:, :12])) + history[:, -1, :] = obs + 1 + self.assertTrue(torch.equal(runtime.infer(obs, history), 2 * obs[:, :12] + 1)) + self.assertIs(runtime._process, process) + runtime.close() + self.assertIsNotNone(process.poll()) + self.assertTrue(process.stdout.closed and process.stdin.closed) + runtime.close() # Repeated cleanup is safe. + with self.assertRaisesRegex(RuntimeError, "closed"): + runtime.infer(obs, history) + + def test_bad_handshake_and_startup_failure_are_reported(self): + for mode, message in [("bad_header", "protocol"), ("startup_failure", "closed output")]: + with self.subTest(mode=mode): + self.engine.write_text(mode) + with self.assertRaisesRegex(RuntimeError, message): + TensorRTWorker(self.worker, self.engine, self.cfg) + + def test_truncated_action_does_not_hang(self): + runtime = self.start("short_output") + with self.assertRaisesRegex(RuntimeError, "closed output"): + runtime.infer(torch.zeros(1, 49), torch.zeros(1, 50, 49)) + + def test_nonfinite_output_is_rejected(self): + runtime = self.start("nan") + with self.assertRaisesRegex(RuntimeError, "non-finite"): + runtime.infer(torch.zeros(1, 49), torch.zeros(1, 50, 49)) + + def test_stalled_worker_times_out_and_can_be_stopped(self): + runtime = self.start("stall", timeout_s=0.5) + with self.assertRaises(TimeoutError): + runtime.infer(torch.zeros(1, 49), torch.zeros(1, 50, 49)) + runtime.close() + self.assertIsNotNone(runtime._process.poll()) + + def test_invalid_inputs_are_rejected_before_send(self): + runtime = self.start() + with self.assertRaisesRegex(ValueError, "observation shape"): + runtime.infer(torch.zeros(1, 48), torch.zeros(1, 50, 49)) + with self.assertRaisesRegex(ValueError, "history shape"): + runtime.infer(torch.zeros(2, 49), torch.zeros(1, 50, 49)) + with self.assertRaisesRegex(ValueError, "finite"): + runtime.infer(torch.full((1, 49), float("nan")), torch.zeros(1, 50, 49)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/rma/test_training_log.py b/tests/rma/test_training_log.py new file mode 100644 index 000000000..5138dc29f --- /dev/null +++ b/tests/rma/test_training_log.py @@ -0,0 +1,98 @@ +import contextlib +import csv +import io +import sys +import tempfile +import unittest +from pathlib import Path + +import torch +from tensorboard.backend.event_processing.event_accumulator import EventAccumulator + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "rsl_rl")) +from unilab_rma import ActorCriticRMA, AdaptationModule, RMAConfig +from unilab_rma.runner import train_teacher +from unilab_rma.training_log import EpisodeLog, TrainingLog + + +class LoggingTests(unittest.TestCase): + def test_csv_events_and_unique_runs(self): + with tempfile.TemporaryDirectory() as folder: + output = Path(folder) / "teacher.pt" + with TrainingLog(output, "teacher", {"seed": 42}) as log: + directory = log.directory + log.write( + 1, + 48, + loss=0.5, + mean_step_reward=0.1, + completed_episodes=0, + mean_episode_return=None, + mean_episode_length_steps=None, + ) + self.assertEqual( + len(list(csv.DictReader((directory / "metrics.csv").read_text().splitlines()))), + 1, + ) + log.write( + 2, + 96, + loss=0.4, + mean_step_reward=0.2, + completed_episodes=2, + mean_episode_return=1.5, + mean_episode_length_steps=7, + ) + rows = list(csv.DictReader((directory / "metrics.csv").read_text().splitlines())) + self.assertEqual(rows[0]["mean_episode_return"], "") + self.assertEqual(rows[1]["total_transitions"], "96") + events = EventAccumulator(str(directory / "tensorboard")).Reload() + self.assertEqual([event.step for event in events.Scalars("teacher/loss")], [1, 2]) + self.assertEqual(len(events.Scalars("teacher/mean_episode_return")), 1) + with TrainingLog(output, "teacher", {}) as second: + self.assertNotEqual(second.directory, directory) + + def test_episode_accumulators_cross_rollouts_and_reset_individually(self): + episodes = EpisodeLog(2) + first = episodes.consume( + torch.tensor([[1.0, 2.0], [3.0, 4.0]]), torch.tensor([[False, True], [False, False]]) + ) + self.assertEqual(first["mean_episode_return"], 2.0) + second = episodes.consume(torch.tensor([[5.0, 6.0]]), torch.tensor([[True, True]])) + self.assertEqual(second["completed_episodes"], 2) + self.assertEqual(second["mean_episode_return"], 9.5) + self.assertEqual(second["mean_episode_length_steps"], 2.5) + + def test_logging_does_not_change_policy_updates_or_rng(self): + class Env: + num_envs = 2 + + def reset(self): + return self.obs() + + def obs(self): + return torch.randn(2, 49), torch.randn(2, 52), torch.ones(2, 2) + + def step(self, action): + done = torch.zeros(2, dtype=torch.bool) + return self.obs(), -action.square().mean(-1), done, done, self.obs() + + def run(output, logging): + torch.manual_seed(123) + teacher, adaptation = ActorCriticRMA(RMAConfig()), AdaptationModule(RMAConfig()) + scope = TrainingLog(output, "teacher", {}) if logging else contextlib.nullcontext(None) + with contextlib.redirect_stdout(io.StringIO()), scope as logger: + train_teacher(Env(), teacher, adaptation, 2, output, horizon=3, logger=logger) + return teacher.state_dict(), torch.get_rng_state() + + with tempfile.TemporaryDirectory() as folder: + expected, rng1 = run(Path(folder) / "off.pt", False) + actual, rng2 = run(Path(folder) / "on.pt", True) + self.assertTrue(torch.equal(rng1, rng2)) + self.assertTrue(all(torch.equal(expected[name], actual[name]) for name in expected)) + + +if __name__ == "__main__": + torch.set_num_threads(1) + unittest.main()