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

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:

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.

  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:

  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:

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.

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； URDF、mesh 与简化碰撞体约定见 Gen2 资产说明。

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 <name>  
 python scripts/play.py  --task=Flat-PM01-v0 --num_envs 128 --load_run <name> 

Gen2 Staged Training

基础阶段从头训练：

/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：

/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 '<fast_run_directory>' \
  --checkpoint model_<iteration>.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 <name> 
 python scripts/play.py --task=Flat-AMP-PM01-v0 --num_envs 128 --load_run <name>

Replace <name> 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

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

./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.

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 page.
• Email Us: For general inquiries or collaboration opportunities, feel free to reach out at 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 — The foundational framework for training and running simulation experiments.
• rsl_rl — High-performance reinforcement learning library for legged robots.
• AMP_for_hardware — Implementation of Adversarial Motion Priors (AMP) for sim-to-real transfer.
• BeyondMimic — Inspiration for project structure and valuable feature implementations.
• MNN — Lightweight, high-performance inference engine for on-device deployment.
• engineai_robotics_native_sdk — Official SDK for deploying policies on EngineAI robotic hardware.