# Mini-Pi-Plus_BeyondMimic **Repository Path**: RadishRoc/Mini-Pi-Plus_BeyondMimic ## Basic Information - **Project Name**: Mini-Pi-Plus_BeyondMimic - **Description**: Beyondmimic - **Primary Language**: Python - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-11-28 - **Last Updated**: 2025-11-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Mini Pi Plus based on BeyondMimic [![IsaacSim](https://img.shields.io/badge/IsaacSim-5.0.0-silver.svg)](https://docs.isaacsim.omniverse.nvidia.com/5.0.0/installation/download.html) [![Isaac Lab](https://img.shields.io/badge/IsaacLab-2.2.0-silver)](https://isaac-sim.github.io/IsaacLab/v2.2.0/index.html) [![Python](https://img.shields.io/badge/python-3.11-blue.svg)](https://docs.python.org/3/whatsnew/3.11.html) [![Linux platform](https://img.shields.io/badge/platform-linux--64-orange.svg)](https://releases.ubuntu.com/20.04/) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://pre-commit.com/) [![License](https://img.shields.io/badge/license-MIT-yellow.svg)](https://opensource.org/license/mit) ![gif](https://github.com/Daily-study-HT/bydmimic_publish/blob/main/gif/6363667a0f27da450e1059a30c2b274b.gif) ## Introduction This project is modified and built based on [Beyondmimic](https://github.com/HybridRobotics/whole_body_tracking). The original project, proposed by author qiayuanl, is a versatile humanoid robot control framework. It can provide highly dynamic motion tracking and state-of-the-art motion quality in real-world deployment, along with controllable test-time control through a guidance diffusion-based controller. This repo includes motion tracking training, provides assets for HighTorque Robotics robots, and optimizes configuration parameters specifically for HighTorque Robotics robots. **You can train sim-to-real motion using the datasets in the provided motion folder without adjusting any parameters**. ## Installation Install Isaac Sim v5.0.0, Isaac Lab v2.2.0, and rsl_rl 2.3.1 following the [installation guide](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html). We recommend using Conda for installation as it simplifies calling Python scripts from the terminal (Note: Isaac Sim v4.5 and Isaac Lab v2.1.0 environments are also compatible). - Clone this repository separately from the Isaac Lab installation directory (i.e., outside the `IsaacLab` directory): ```bash git clone https://github.com/HighTorque-Robotics/Mini-Pi-Plus_BeyondMimic ``` - Install the project using the Conda virtual environment where Isaac Lab is installed. Navigate to the project directory and execute: ```bash python -m pip install -e source/whole_body_tracking ``` ## Motion Tracking ### Obtain Data & GMR Retargeting Data Use the GMR project for dataset retargeting. Original project address: https://github.com/YanjieZe/GMR ```bash # Create a Conda environment. For avoiding environment conflicts, retargeting is performed in an independent virtual environment conda deactivate conda create -n gmr python=3.10 -y conda activate gmr pip install -e GMR conda install -c conda-forge libstdcxx-ng -y ``` **Note: If you are not using the GMR provided in this project but downloaded the content from the original link, modify `numpy==1.24.4` in setup.py before installation.** **Note: To facilitate your use of HighTorque Robotics robots, we provide CSV-format retargeted data files and converted NPZ-format file templates in `source/motion`. If you need to retarget other files, you can follow the steps below.** ### Motion Preprocessing & Registry Setup For easy data inspection: ```bash # Retargeting python scripts/bvh_to_robot.py --bvh_file MotionData/lafan1/{xxx}.bvh --robot pi_football --save_path RetargetData/lafan1/csv/pi_plus/{xxx}.csv --rate_limit # Trimming python scripts/csv_cut.py --input_csv RetargetData/lafan1/csv/pi_plus/pi_plus_dance1_subject2.csv --output_csv RetargetData/lafan1/csv/pi_plus/{xxx}.csv --start_frame {number} --end_frame {number} --remove_frame_column --z_offset 0.00 --decimal_places 6 # NPZ format conversion conda activate {your_env_isaaclab} python scripts/csv_to_npz.py --robot pi_plus --input_file source/motion/hightorque/pi_plus/csv/xxx.csv --input_fps 30 --output_name source/motion/hightorque/pi_plus/npz/{motion_name} # Add --headless if you don't want to load the graphical interface # Data playback python scripts/replay_npz.py --robot pi_plus --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz ``` ### Model Training Train the policy with the following command: ```bash python scripts/rsl_rl/train.py --task=Tracking-Flat-PI-Plus-Wo-v0 --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz --headless --log_project_name pi_plus_beyondmimic # Remove --logger wandb if you don't want to use wandb # Resume training with --resume {load_run_name} ``` ### Model Export Play the trained policy with the following command: ```bash python scripts/rsl_rl/play.py --task=Tracking-Flat-PI-Plus-Wo-v0 --checkpoint {logs_path_to}/model_xxx.pt --num_envs=1 --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz ``` ![if](https://github.com/Daily-study-HT/bydmimic_publish/blob/main/gif/e7faf89fbdbf87cf909bbf81ceeb1a7f.gif) ### Model Evaluation Validate the policy in Mujoco with the following command: ```bash python scripts/sim2sim.py --robot pi_plus --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz --xml_path source/whole_body_tracking/whole_body_tracking/assets/hightorque/pi_plus/mjcf/pi_20dof.xml --policy_path {logs_path_to}/exported/{model_xxx}.onnx --save_json --loop # Use --loop to play the policy repeatedly ``` ![f](https://github.com/Daily-study-HT/bydmimic_publish/blob/main/gif/de78f3ab232911f9a93e936cb5463164.gif) ## Code Structure The following is an overview of the project's code structure: - **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/mdp`** This directory contains atomic functions for defining BeyondMimic's MDP. Below is a breakdown of these functions: - **`commands.py`** Command library for calculating relevant variables based on reference motion, current robot state, and errors. Calculations include pose and velocity error computation, initial state randomization, and adaptive sampling. - **`rewards.py`** Implements DeepMimic reward functions and smoothing terms. - **`events.py`** Implements domain randomization terms. - **`observations.py`** Implements observation terms for motion tracking and data collection. - **`terminations.py`** Implements early termination and timeouts. - **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/tracking_env_cfg.py`** Contains environment (MDP) hyperparameter configurations for tracking tasks. - **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/config/g1/agents/rsl_rl_ppo_cfg.py`** Contains PPO hyperparameters for tracking tasks. - **`source/whole_body_tracking/whole_body_tracking/robots`** Contains robot-specific settings, including skeleton parameters, joint stiffness/damping calculations, and action scaling computations. - **`scripts`** Includes utility scripts for preprocessing motion data, training policies, and evaluating trained policies. This structure is designed to ensure modularity and ease of navigation for developers extending the project. #### Original Project Repository [whole_body_tracking](https://github.com/HybridRobotics/whole_body_tracking) # Mini Pi Plus based on BeyondMimic （中文翻译） ## 介绍 此项目是基于 [Beyondmimic](https://github.com/HybridRobotics/whole_body_tracking) 进行修改建立的。原项目是由作者qiayuanl提出的一个多功能的人形机器人控制框架，它能够在实际部署中提供高度动态的运动跟踪和最先进的运动质量，并通过基于引导扩散的控制器提供可控的测试时间控制。 此 repo 涵盖了运动追踪训练，提供了高擎机电机器人的asset，并针对高擎机电机器人进行了配置参数的调优。**您能够 在所提供的motion文件夹下的数据集中训练可模拟到现实的运动，而无需调整任何参数**。 ## 安装 按照[安装指南](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html) 安装 Isaac Sim v5.0.0 、 Isaac Lab v2.2.0 和 rsl_rl 2.3.1。我们建议使用 conda 安装，因为它可以简化从终端调用 Python 脚本的操作（备注:Isaac Sim v4.5,Isaac Lab v2.1.0环境也可以）。 - 将此代码库与 Isaac Lab 安装目录分开克隆（即，克隆到`IsaacLab`目录之外）： ```bash git clone https://github.com/HighTorque-Robotics/Mini-Pi-Plus_BeyondMimic ``` - 使用安装了 Isaac Lab 的 Conda 虚拟环境安装本项目，进入项目目录后，执行 ```bash python -m pip install -e source/whole_body_tracking ``` ## 动作跟踪 ### 获取数据，GMR重定向数据 使用GMR项目进行数据集的重定向，原项目地址: https://github.com/YanjieZe/GMR ``` # create conda env 为避免环境冲突等问题，重定向在一个独立的虚拟环境中进行 conda deactivate conda create -n gmr python=3.10 -y conda activate gmr pip install -e GMR conda install -c conda-forge libstdcxx-ng -y ``` **注：如果没有使用本项目提供的GMR而是是下载的原链接内容，注意安装前修改setup.py 中 numpy==1.24.4。** **注：为了便于您使用高擎机电的机器人，我们在`source/motion`中提供了csv版本的重定向数据文件和转换好的npz格式文件模板供你使用，若需要重定向其他文件可以自行按照下方步骤执行** ### Motion Preprocessing & Registry Setup 为了便于检查数据内容， ```bash # 重定向 python scripts/bvh_to_robot.py --bvh_file MotionData/lafan1/{xxx}.bvh --robot pi_football --save_path RetargetData/lafan1/csv/pi_plus/{xxx}.csv --rate_limit # 裁剪 python scripts/csv_cut.py --input_csv RetargetData/lafan1/csv/pi_plus/pi_plus_dance1_subject2.csv --output_csv RetargetData/lafan1/csv/pi_plus/{xxx}.csv --start_frame {number} --end_frame {number} --remove_frame_column --z_offset 0.00 --decimal_places 6 # npz格式转换 conda activate {your_env_isaaclab} python scripts/csv_to_npz.py --robot pi_plus --input_file source/motion/hightorque/pi_plus/csv/xxx.csv --input_fps 30 --output_name source/motion/hightorque/pi_plus/npz/{motion_name} #若不想加载图形化界面则添加参数 --headless # 数据播放 python scripts/replay_npz.py --robot pi_plus --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz ``` ### 模型训练 通过以下命令训练策略： ```bash python scripts/rsl_rl/train.py --task=Tracking-Flat-PI-Plus-Wo-v0 --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz --headless --log_project_name pi_plus_beyondmimic #若不想使用wandb，可以去掉 --logger wandb #继续训练 --resume {load_run_name} ``` ### 模型导出 通过以下命令play训练好的策略： ```bash python scripts/rsl_rl/play.py --task=Tracking-Flat-PI-Plus-Wo-v0 --checkpoint {logs_path_to}/model_xxx.pt --num_envs=1 --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz ``` ![if](https://github.com/Daily-study-HT/bydmimic_publish/blob/main/gif/e7faf89fbdbf87cf909bbf81ceeb1a7f.gif) ### 模型评估 通过以下命令在mujoco中验证策略： ```bash python scripts/sim2sim.py --robot pi_plus --motion_file source/motion/hightorque/pi_plus/npz/{motion_name}.npz --xml_path source/whole_body_tracking/whole_body_tracking/assets/hightorque/pi_plus/mjcf/pi_20dof.xml --policy_path {logs_path_to}/exported/{model_xxx}.onnx --save_json --loop #使用--loop可以循环播放策略 ``` ![f](https://github.com/Daily-study-HT/bydmimic_publish/blob/main/gif/de78f3ab232911f9a93e936cb5463164.gif) ## 代码结构 以下是此项目的代码结构概述： - **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/mdp`** 此目录包含用于定义 BeyondMimic 的 MDP 的原子函数。以下是这些函数的细分： - **`commands.py`** 命令库用于根据参考运动、当前机器人状态和误差计算相关变量。 计算包括位姿和速度误差计算、初始状态随机化和自适应采样。 - **`rewards.py`** 实现 DeepMimic 奖励函数和平滑项。 - **`events.py`** 实现域随机化项。 - **`observations.py`** 实现运动跟踪和数据收集的观测项。 - **`terminations.py`** 实现提前终止和超时。 - **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/tracking_env_cfg.py`** 包含跟踪任务的环境（MDP）超参数配置。 - **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/config/g1/agents/rsl_rl_ppo_cfg.py`** 包含跟踪任务的 PPO 超参数。 - **`source/whole_body_tracking/whole_body_tracking/robots`** 包含机器人特定设置，包括骨架参数、关节刚度/阻尼计算和动作比例计算。 - **`scripts`** 包括用于预处理运动数据、训练策略和评估训练策略的实用脚本。 该结构旨在确保开发人员扩展项目的模块化和易于寻找。 ## 原项目地址 [whole_body_tracking](https://github.com/HybridRobotics/whole_body_tracking)