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Aversatile, all-in-one toolbox for whole-body humanoid robot control—enabling universal motion tracking, upper–lower body split strategies, and accelerated experimentation across simulation and real-world platforms.

• Whole Body Control Mode: Effortlessly track full-body human motions in azero-shot fashion—generalize, don’t overfit.
• Upper–Lower Body Split Mode: Enhanced control strategy likeHomie with dynamic walking and powerful manipulation—seamless coordination, robust skills.
• Multi-Robot Ready: Instantly deploy onUnitree G1,H1,H1-2, andFourier GR-1—with more robots joining the lineup!
• Lightning-Fast Experimentation: Tweak everything with flexible Hydra configs—adapt, iterate, and innovate at speed.
• Sim-to-Real Mastery: Built-in friction & mass randomization, noisy observations, and Sim2Sim testing—engineered for real-world success.

• [2025/07] First Release for Universal Humanoid Motion Tracking on Unitree G1!

• Release Whole Body Control Mode on Unitree G1
• Release Upper–Lower Body Split Mode on Unitree G1
• Release Pre-trained Checkpoints and Training Data
• Release Environments on Different Robots
• Release Deployment Codes

• 🚀 Highlights
• 📰 News
• 🚧 TODO
• ⚡ Quick Start
• 🛠️ Installation
• 🗂️ Code Structure
• 🧩 Adding New Environments
• 🔗 Citation
• 📄 License
• 👏 Acknowledgements

The typical workflow for controlling real-world humanoid robots with InternHumanoid:

Train →Play →Sim2Sim →Sim2Real

Train the universal motion tracker for Unitree G1-29 DoF:

python legged_gym/scripts/train.py +algo=ppo +robot=g1/g1_29dof +task=imitation/g1_29dof

• To run on CPU: add+sim_device=cpu +rl_device=cpu
• To run headless (no rendering): add+headless
• Trained policies are saved inlogs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt

After training, play the saved checkpoint:

python legged_gym/scripts/play.py +algo=ppo +robot=g1/g1_29dof +task=imitation/g1_29dof

• By default, loads the last model of the last run in the experiment folder.

Test the saved ONNX model with sim2sim transfer (Mujoco as the testing environment):

cd sim2simpython play_im.py --robot g1_29dof

More details of training and playing can be found in thedocumentation.

Please refer to theinstallation guide for detailed steps and configuration instructions.

• envs/ : Environment/task definitions
• config/ : YAML configuration files for tasks, robots, terrains, algorithms
• utils/ : Math, logging, motion libraries, terrain helpers, task registry
• scripts/ : Entry-point scripts for training, playing, and exporting models

• algorithms/ : RL algorithms (e.g., PPO variants)
• modules/ : Neural network modules (actor-critic, normalization, etc.)
• runners/ : Training and evaluation runners
• env/ : Environment wrappers and vectorized interfaces
• storage/ : Rollout storage and replay buffers
• utils/ : Utility functions and experiment helpers

To add a new simulation environment or modify configuration files, seeadd new experiments.md for a step-by-step guide and detailed examples.

If you find our work helpful, please cite:

@misc{internhumanoid2025,title ={InternHumanoid: Universal Whole-Body Control and Imitation for Humanoid Robots},author ={InternHumanoid Contributors},howpublished={\url{https://github.com/InternRobotics/InternHumanoid}},year ={2025}}

InternHumanoid isMIT licensed.
Open-sourced data are under theCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

• legged_gym: Foundation for training and running codes.
• rsl_rl: Reinforcement learning algorithms.
• mujoco: Powerful simulation functionalities.
• unitree_rl: Powerful reinforcement learning framework provided for Unitree Robots.
• unitree_sdk2_python: Hardware communication interface for physical deployment.

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