TienKung-Lab

Overview

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. The framework has also been successfully validated on the real TienKung robot.

Installation

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

• Install Isaac Lab by following the official installation guide. We recommend using the conda installation.
• Clone this repository separately from the Isaac Lab installation.
• 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 .

Usage Examples

Visualize Motion

Visualize the motion by updating the simulation with data from the datasets.

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

Train

Train the policy using AMP expert data.

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

Sim2Real Verification

The results of the TienKung-Lab have been successfully verified on the real TienKung robot.