# EngineAI-Lab **EngineAI Lab** is a python package for training and deploying policies for EngineAI Robots using Isaac Lab and Isaac Sim. |Training| Sim2Sim |Deploy| |--------|--------|--------| ![training](./docs/training.gif)|![sim2sim](./docs/sim2sim.gif)| # Structure ``` engineai-lab ├── config ├── dataset │ ├── config │ └── data ├── scripts └── source └── engineai_lab ├── algorithms ├── assets │ ├── gen2 │ │ ├── meshes │ │ └── urdf │ └── pm01 │ ├── meshes │ └── urdf ├── robots ├── tasks │ └── velocity │ ├── config │ │ ├── common │ │ ├── gen2 │ │ │ ├── agents │ │ │ └── stages │ │ └── pm01 │ └── mdp └── utils ``` ## QUICKSTART ### 1. Create a Conda Environment Create and activate a new environment with Python 3.11: ```bash conda create -n engineai_lab python=3.11 conda activate engineai_lab ``` ### 2. Install Prerequisites - **Install Isaac Sim** Follow the official installation guide: [Isaac Lab - Pip Installation](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/pip_installation.html#installing-dependencies). *Since you've already created the engineai_lab environment, follow the guide from "Installing Dependencies" up to (but not including) the "Installing Isaac Lab" section.* - **Clone & Setup Isaac Lab** Clone the repository and switch to the recommended branch: ```bash git clone https://github.com/isaac-sim/IsaacLab.git cd IsaacLab git checkout 4df6560e ./isaaclab.sh -i rsl_rl # Install rsl-rl dependency ``` We highly recommend using the main branch`(4df6560e)` of Isaac Lab, as it can support rsl-rl-lib >= 5.0 and Isaac Sim >= 5.0 . ### 3. Install this Package Once the prerequisites are set up, install the package in editable mode: ```bash pip install -e . ``` ## Usage For a step-by-step Chinese guide to importing and training a new humanoid robot, see [EngineAI Lab 新人形机器人行走训练攻略](docs/humanoid_locomotion_onboarding_guide.md). Gen2 的阶段划分、训练/续训/迁移命令和配置修改约定见 [Gen2 分阶段训练说明](source/engineai_lab/tasks/velocity/config/gen2/README.md)； URDF、mesh 与简化碰撞体约定见 [Gen2 资产说明](source/engineai_lab/assets/gen2/README.md)。 ### Supported Robots This repository currently supports the following environments from the EngineAI Robots family: |Robot| Task |Description| |--------|--------|--------| PM01|`Flat-PM01-v0`|Basic flat-terrain locomotion PM01|`Flat-AMP-PM01-v0`|AMP-based motion imitation on flat terrain Gen2|`Flat-Gen2-v0`|基础低速行走，包含站立指令采样 Gen2|`Flat-Gen2-Speed-v0`|平地速度与稳定性巩固 Gen2|`Flat-Gen2-Natural-v0`|自然对侧摆臂 Gen2|`Flat-Gen2-Fast-v0`|课程提升至 1.6 m/s Gen2|`Flat-Gen2-Sprint-v0`|课程提升至 3.0 m/s Gen2|`Flat-Gen2-NaturalRun-v0`|3.0 m/s 自然跑姿微调 *More robots and environments are coming soon!* ### Training a Policy ``` python scripts/train.py --task=Flat-PM01-v0 --num_envs 4096 --headless --run_name python scripts/play.py --task=Flat-PM01-v0 --num_envs 128 --load_run ``` ### Gen2 Staged Training 基础阶段从头训练： ```bash /home/xtkuang/App/anaconda3/envs/engineai_lab/bin/python scripts/train.py \ --task Flat-Gen2-v0 \ --num_envs 4096 \ --max_iterations 1500 \ --run_name gen2_walk_v0 \ --device cuda:0 \ --rl_device cuda:0 \ --headless ``` 跨阶段只迁移策略权重，例如 Fast -> Sprint： ```bash /home/xtkuang/App/anaconda3/envs/engineai_lab/bin/python scripts/train.py \ --task Flat-Gen2-Sprint-v0 \ --num_envs 4096 \ --resume True \ --load_mode finetune \ --load_run '' \ --checkpoint model_.pt \ --run_name gen2_sprint_v1 \ --device cuda:0 \ --rl_device cuda:0 \ --headless ``` 同一 Task 中断续训才使用 `--load_mode resume`。所有阶段和对应 Play Task 的完整表格见上面的 Gen2 分阶段训练说明。 ### Evaluating a Policy ``` python scripts/train.py --task=Flat-AMP-PM01-v0 --num_envs 4096 --headless --run_name python scripts/play.py --task=Flat-AMP-PM01-v0 --num_envs 128 --load_run ``` Replace `` with the name of your training run (found in logs/rsl_rl/). ### Deployment To deploy a trained policy on real hardware, convert it to the MNN format for efficient inference. #### 1. Export PyTorch Policy to ONNX (Ensure your training script supports ONNX export) #### 2. Build [MNN-Converter](https://mnn-docs.readthedocs.io/en/latest/start/quickstart_cpp.html?highlight=mnn+converter) ```bash git clone https://github.com/alibaba/mnn cd mnn mkdir build && cd build cmake .. -DMNN_BUILD_CONVERTER=ON make -j8 ``` #### 3. Convert ONNX into MNN ```bash ./MNNConvert -f ONNX \ --modelFile path_to_your_policy.onnx \ --MNNModel your_policy.mnn \ --bizCode MNN ``` For detailed instructions on integrating the MNN model with EngineAI robots, see: [engineai_robotics_native_sdk](https://github.com/engineai-robotics/engineai_robotics_native_sdk). ## Support If you have any questions about using this repository, we're here to help! - **Report Issues**: Found a bug or have a feature request. Please open a new issue on our [GitHub Issues](https://github.com/engineai-robotics/engineai_lab/issues) page. - **Email Us**: For general inquiries or collaboration opportunities, feel free to reach out at [info@engineai.com.cn](mailto:info@engineai.com.cn). ## License This checkout does not currently contain a `LICENSE` file, while its package metadata and the upstream README use different license labels. Add a license file and align the metadata before redistribution. ## Acknowledgement This repository is built upon the support and contributions of the following open-source projects. Special thanks to: - [**IsaacLab**](https://github.com/isaac-sim/IsaacLab) — The foundational framework for training and running simulation experiments. - [**rsl_rl**](https://github.com/leggedrobotics/rsl_rl) — High-performance reinforcement learning library for legged robots. - [**AMP_for_hardware**](https://github.com/escontra/AMP_for_hardware) — Implementation of Adversarial Motion Priors (AMP) for sim-to-real transfer. - [**BeyondMimic**](https://github.com/HybridRobotics/whole_body_tracking) — Inspiration for project structure and valuable feature implementations. - [**MNN**](https://github.com/alibaba/mnn) — Lightweight, high-performance inference engine for on-device deployment. - [**engineai_robotics_native_sdk**](https://github.com/engineai-robotics/engineai_robotics_native_sdk) — Official SDK for deploying policies on EngineAI robotic hardware.