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VIP-Loco: A Visually Guided Infinite Horizon Planning Framework for Legged Locomotion

Installation

This repository is a modified version of WMP-Loco. Modifications roughly include our method, the Warp backend for fast depth processing, JAX for planning, and additional robot platforms.

Prerequisites

  • Ubuntu 20.04 / 22.04
  • CUDA 12.1+
  • Conda

1. Clone the repo

git clone https://github.com/StochLab/VIP-Loco
cd VIP-Loco

2. Create the conda environment

conda create -n viploco python=3.8
conda activate viploco

3. Install dependencies

pip install torch==2.3.0 torchvision --index-url https://download.pytorch.org/whl/cu121
pip install jax[cuda12]==0.4.13 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install warp-lang==1.4.1
pip install -r requirements.txt

4. Install IsaacGym

Download IsaacGym Preview 4 and install it manually inside the conda environment:

cd <isaacgym_path>/python
pip install -e .

Usage

Training

conda activate viploco
cd VIP-Loco
python legged_gym/scripts/train.py --headless --sim_device=cuda:0 --wm_device=cuda:0 --task=go1_amp

Supported tasks: go1_amp, cassie, trona1_w

Evaluation

# With planner
python legged_gym/scripts/play_plan.py --sim_device=cuda:0 --wm_device=cuda:0 --task=go1_amp --terrain=slope

# Without planner
python legged_gym/scripts/play.py --sim_device=cuda:0 --wm_device=cuda:0 --task=go1_amp --terrain=slope

Supported terrains: slope, stair, gap, climb, crawl, tilt

Issues

  • The code is not in a release version. If you face any issues during installation or while running the scripts, please feel free to create an issue :)
  • When this project was in development, there was no reliable or robust way to make PyTorch models run inside jax.jit. As a result, a torch2jax file was taken from torch2jax and modified according to our needs. However, there is now a better library called torchax, which is a more robust and faster method. Please migrate to it if necessary.

Acknowledgements

Built on top of WMP-Loco and legged_gym.