Official implementation of
Hierarchical World Models as Visual Whole-Body Humanoid Controllers by
Nicklas Hansen, Jyothir S V, Vlad Sobal, Yann LeCun, Hao Su*, Xiaolong Wang*
UC San Diego, NYU, Meta AI
*Equal advising
We present Puppeteer, a hierarchical world model for whole-body humanoid control with visual observations. Our method produces natural and human-like motions without any reward design or skill primitives, and traverses challenging terrain.
This repository contains code for training and evaluating both low-level (tracking) and high-level (puppeteering) world models. We open-source model checkpoints for both levels of the hierarchy, so that you can get started without training any models yourself. Model checkpoints are available for download here.
You will need a machine with a GPU (>= 24 GB memory) for training; CPU and RAM usage is insignificant. We provide a Dockerfile for easy installation. You can build the docker image by running
cd docker && docker build . -t <user>/puppeteer:1.0.0
This docker image contains all dependencies needed for running training and inference.
If you prefer to install dependencies manually, start by installing dependencies via conda by running the following command:
conda env create -f docker/environment.yaml
Depending on your existing system packages, you may need to install other dependencies. See docker/Dockerfile for a list of recommended system packages.
This codebase currently supports 8 whole-body control tasks for the CMU Humanoid model, implemented in MuJoCo using DMControl. The tasks are defined as follows:
| task | vision |
|---|---|
| stand | N |
| walk | N |
| run | N |
| corridor | Y |
| hurdles-corridor | Y |
| gaps-corridor | Y |
| walls-corridor | Y |
| stairs-corridor | Y |
which can be run by specifying the task argument for train.py and evaluation.py.
We provide examples on how to evaluate our provided Puppeteer model checkpoints, as well as how to train your own Puppeteer agents, below.
See below examples on how to evaluate downloaded low-level and high-level checkpoints.
$ python evaluate.py task=corridor low_level_fp=/path/to/tracking.pt checkpoint=/path/to/corridor-1.pt
$ python evaluate.py task=gaps-corridor low_level_fp=/path/to/tracking.pt checkpoint=/path/to/gaps-corridor-1.pt
All high-level checkpoints were trained with the same low-level checkpoint. See config.yaml for a full list of arguments.
See below examples on how to train low-level and high-level world models for Puppeteer. We recommend configuring Weights and Biases (wandb) in config.yaml to track training progress.
$ python train.py task=tracking
$ python train.py task=walk low_level_fp=/path/to/tracking.pt
$ python train.py task=corridor low_level_fp=/path/to/tracking.pt
We recommend using default hyperparameters for all tasks. See config.yaml for a full list of arguments.
If you find our work useful, please consider citing our paper as follows:
@misc{hansen2024hierarchical,
title={Hierarchical World Models as Visual Whole-Body Humanoid Controllers},
author={Nicklas Hansen, Jyothir S V, Vlad Sobal, Yann LeCun, Xiaolong Wang, Hao Su},
eprint={2405.18418},
archivePrefix={arXiv},
primaryClass={cs.LG},
year={2024}
}
You are very welcome to contribute to this project. Feel free to open an issue or pull request if you have any suggestions or bug reports, but please review our guidelines first.
This project is licensed under the MIT License - see the LICENSE file for details. Note that the repository relies on third-party code, which is subject to their respective licenses.