TienKung humanoid robot won the championship in the first Humanoid Robot Half Marathon

Motion	AMP Animation	Sensors	RL + AMP	Sim2Sim
Walk
Run

This framework is an RL-based locomotion control system designed for full-sized humanoid robots, TienKung. It integrates AMP-style rewards with periodic gait rewards, facilitating natural, stable, and efficient walking and running behaviors.

The codebase is built on IsaacLab, supports Sim2Sim transfer to MuJoCo, and features a modular architecture for seamless customization and extension. Additionally, it incorporates ray-casting-based sensors for enhanced perception, enabling precise environmental interaction and obstacle avoidance.

• Motion retargeting support 2025-09-27
• Add more sensors
• Add Perceptive Control

TienKung-Lab is built with IsaacSim 4.5.0 and IsaacLab 2.1.0.

• Install Isaac Lab by following the installation guide. We recommend using the conda installation as it simplifies calling Python scripts from the terminal.

• Clone this repository separately from the Isaac Lab installation (i.e. outside the IsaacLab directory)

• Using a python interpreter that has Isaac Lab installed, install the library

cd TienKung-Lab
pip install -e .

• Install the rsl-rl library

cd TienKung-Lab/rsl_rl
pip install -e .

• Verify that the extension is correctly installed by running the following command:

python legged_lab/scripts/train.py --task=walk  --logger=tensorboard --headless --num_envs=64

AMASS	GMR	TIENKUNGLAB

This section uses GMR for motion retargeting, Tienkung currently supports motion retargeting only for SMPLX types (AMASS, OMOMO).

1. Prepare the dataset and Motion retargeting with GMR.

python scripts/smplx_to_robot.py --smplx_file <path_to_smplx_data> --robot tienkung  --save_path <path_to_save_robot_data.pkl>

2. Data Processing and Data Saving.

The dataset consists of two parts with distinct functions and formats, requiring conversion in two steps.

• motion_visualization/
  Used for motion playback with play_amp_animation.py to check motion correctness and quality.
  Data fields: [root_pos, root_rot, dof_pos, root_lin_vel, root_ang_vel, dof_vel]

• motion_amp_expert/
  Used during training as expert reference data for AMP.
  Data fields: [dof_pos, dof_vel, end-effector pos]

• Step 1: Data Processing and Visualization Data Saving.

python legged_lab/scripts/gmr_data_conversion.py --input_pkl <path_to_save_robot_data.pkl> --output_txt legged_lab/envs/tienkung/datasets/motion_visualization/motion.txt

Note: Before starting step 2, set the amp_motion_files_display path in the config to the file generated in step 1.

• Step 2: Motion Visualization and Expert Data Saving.

python legged_lab/scripts/play_amp_animation.py --task=walk --num_envs=1 --save_path legged_lab/envs/tienkung/datasets/motion_amp_expert/motion.txt --fps 30.0

Note: After step 2, set the amp_motion_files path in the config to the file generated in step 2.

Visualize the motion by updating the simulation with data from tienkung/datasets/motion_visualization.

python legged_lab/scripts/play_amp_animation.py --task=walk --num_envs=1
python legged_lab/scripts/play_amp_animation.py --task=run --num_envs=1

Visualize the motion with sensors by updating the simulation with data from tienkung/datasets/motion_visualization.

python legged_lab/scripts/play_amp_animation.py --task=walk_with_sensor --num_envs=1
python legged_lab/scripts/play_amp_animation.py --task=run_with_sensor --num_envs=1

Train the policy using AMP expert data from tienkung/datasets/motion_amp_expert.

python legged_lab/scripts/train.py --task=walk --headless --logger=tensorboard --num_envs=4096
python legged_lab/scripts/train.py --task=run --headless --logger=tensorboard --num_envs=4096

Run the trained policy.

python legged_lab/scripts/play.py --task=walk --num_envs=1
python legged_lab/scripts/play.py --task=run --num_envs=1

Evaluate the trained policy in MuJoCo to perform cross-simulation validation.

Exported_policy/ contains pretrained policies provided by the project. When using the play script, trained policy is exported automatically and saved to path like logs/run/[timestamp]/exported/policy.pt.

python legged_lab/scripts/sim2sim.py --task walk --policy Exported_policy/walk.pt --duration 10
python legged_lab/scripts/sim2sim.py --task run --policy Exported_policy/run.pt --duration 10

tensorboard --logdir=logs/walk
tensorboard --logdir=logs/run

We have a pre-commit template to automatically format your code. To install pre-commit:

pip install pre-commit

Then you can run pre-commit with:

pre-commit run --all-files

In some VsCode versions, the indexing of part of the extensions is missing. In this case, add the path to your extension in .vscode/settings.json under the key "python.analysis.extraPaths".

{
    "python.analysis.extraPaths": [
        "${workspaceFolder}/legged_lab",
        "<path-to-IsaacLab>/source/isaaclab_tasks",
        "<path-to-IsaacLab>/source/isaaclab_mimic",
        "<path-to-IsaacLab>/source/extensions",
        "<path-to-IsaacLab>/source/isaaclab_assets",
        "<path-to-IsaacLab>/source/isaaclab_rl",
        "<path-to-IsaacLab>/source/isaaclab",
    ]
}

• GMR:General Motion Retargeting.
• Legged Lab: a direct IsaacLab Workflow for Legged Robots.
• Humanoid-Gym:a reinforcement learning (RL) framework based on NVIDIA Isaac Gym, with Sim2Sim support.
• RSL RL: a fast and simple implementation of RL algorithms.
• AMP_for_hardware: codebase for learning skills from short reference motions using Adversarial Motion Priors.
• Omni-Perception: a perception library for legged robots, which provides a set of sensors and perception algorithms.
• Warp: a Python framework for writing high-performance simulation and graphics code.

If you're interested in TienKung-Lab, welcome to join our WeChat group for discussions.