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Learning-based locomotion control for H1 humanoid robot using Hybrid Internal Model (HIM) from OpenRobotLab. Fork of original quadruped implementation adapted for bipedal locomotion.

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Demo

descr

Demo locomotion after 1000 epos: Demo

Plots

We test our codes under the following environment:

  • Ubuntu 20.04
  • NVIDIA Driver: 525.147.05
  • CUDA 12.0
  • Python 3.7.16
  • PyTorch 1.10.0+cu113
  • Isaac Gym: Preview 4
  • 4090
  1. Create an environment and install PyTorch:
  • conda create -n himloco python=3.7.16
  • conda activate himloco
  • pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
  1. Install Isaac Gym:
  1. Clone this repository.
  • git clone https://github.com/dancher00/him-humanoid-h1.git
  • cd him-humanoid-h1
  1. Install HIMLoco.
  • cd rsl_rl && pip install -e .
  • cd ../legged_gym && pip install -e .

Note: Please use legged_gym and rsl_rl provided in this repo, we have modefications on these repos.

Tutorial

  1. Train a policy:
  • cd legged_gym/legged_gym/scripts
  • python train.py --task=h1
  1. Play and export the latest policy:
  • cd legged_gym/legged_gym/scripts
  • python play.py --task=h1

About

Learning-based locomotion control for H1 humanoid robot using Hybrid Internal Model (HIM) from OpenRobotLab. Fork of original quadruped implementation adapted for bipedal locomotion.

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