beyondAMP: One-Step Integration of AMP into IsaacLab

Overview

beyondAMP provides a unified pipeline to integrate Adversarial Motion Priors (AMP) into any IsaacLab robot setup, with minimal modifications and full compatibility with custom robot designs. 中文README

🚀 Fast Setup

cd beyondAMP
bash scripts/setup_ext.sh
# Downloads assets, robot configs, and installs dependencies

Optional VSCode workspace setup:

python scripts/setup_vscode.py

📌 How to Use

Quick Start

• Basic environment: source/amp_tasks/amp_tasks/amp
• PPO config for G1 robot: source/amp_tasks/amp_tasks/amp/robots/g1/rsl_rl_ppo_cfg.py

Training can be launched with:

python scripts/factoryIsaac/train.py --task beyondAMP-DemoPunch-G1-BasicAMP --headless
# python scripts/factoryIsaac/train.py --task beyondAMP-DemoPunch-G1-SoftAMPTrack --headless
# python scripts/factoryIsaac/train.py --task beyondAMP-DemoPunch-G1-HardAMPTrack --headless

To evaluate or visualize a trained checkpoint:

python scripts/factoryIsaac/play.py --headless --target <path to your ckpt.pt> --video --num_envs 32

AMP Punch (3k)	Motion Tracking Punck (30k)	AMP Dog Move	AMP Knee Walk

Dataset Preparation

The dataset follows the same structure and conventions used in BeyondMimic(whole_body_tracking). All motion sequences should be stored as *.npz files and placed under data/datasets/, maintaining a consistent directory layout with the reference pipeline.

For motion retargeting and preprocessing, GMR is recommended for generating high-quality retargeted mocap data. TrackerLab may be used to perform forward kinematics checks and robot-specific adjustments, ensuring the motions remain physically plausible for your robot model.

With these tools, the dataset organization naturally aligns with the conventions established in BeyondMimic(whole_body_tracking), enabling seamless integration with the AMP training pipeline.

Following the dataset pipeline of BeyondMimic:

• Motion files: place *.npz into data/datasets/
• Recommended tools:
  • GMR for retargeted motion
  • TrackerLab for FK validation & robot-specific preprocessing

AMP Integration Details

• AMP observation group added via a new amp observation config
• RSL-RL integration: source/rsl_rl/rsl_rl/env/isaaclab/amp_wrapper.py
• Default transition builder: source/beyondAMP/beyondAMP/amp_obs.py

For full tutorial and customization, see docs/tutorial.md.

Additional Notes

• Fully modular AMP observation builder
• Compatible with IsaacLab 4.5+
• Designed for rapid experimentation across robot morphologies

🙏 Acknowledgement

Referenced Repositories

Repository	Purpose
robotlib	Robot configurations
assetslib	Asset storage
TrackerLab	Data organization & retargeting tools
AMP_for_hardware	AMP implementation reference
BeyondMimic	Dataset format & tracking comparison

---

📘 Citation

@software{zheng2025@beyondAMP,
  author = {Ziang Zheng},
  title = {beyondAMP: One step unify IsaacLab with AMP.},
  url = {https://github.com/Renforce-Dynamics/beyondAMP},
  year = {2025}
}

@software{zheng2025@trackerLab,
  author = {Ziang Zheng},
  title = {TrackerLab: One step unify IsaacLab with multi-mode whole-body control.},
  url = {https://github.com/interval-package/trackerLab},
  year = {2025}
}

@INPROCEEDINGS{Escontrela@amphardware,
  author={Escontrela, Alejandro and Peng, Xue Bin and Yu, Wenhao and Zhang, Tingnan and Iscen, Atil and Goldberg, Ken and Abbeel, Pieter},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions}, 
  year={2022}
  }

@article{liao2025beyondmimic,
  title={Beyondmimic: From motion tracking to versatile humanoid control via guided diffusion},
  author={Liao, Qiayuan and Truong, Takara E and Huang, Xiaoyu and Tevet, Guy and Sreenath, Koushil and Liu, C Karen},
  journal={arXiv preprint arXiv:2508.08241},
  year={2025}
}

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