Overview

HumEnv is an environment based on SMPL humanoid which aims for reproducible studies of humanoid control. It is designed to facilitate algorithmic research on reinforcement learning (RL), goal-based RL, unsupervised RL, and imitation learning. It consists of a basic environment interface, as well as an optional benchmark to evaluate agents on different tasks.

Features

• An environment that enables simulation of a realistic humanoid on a range of proprioceptive tasks
• A MuJoCo-based humanoid robot definition tuned for more realistic behaviors (friction, joint actuation, and movement range)
• 9 configurable reward classes to enable learning basic humanoid skills, including locomotion, spinning, jumping, crawling, and more
• Benchmarking code to evaluate RL agents on three classes of tasks: reward-based, goal-reaching and motion tracking
• Various initialisation options: a static "T-pose", random fall, frame from MoCap data, and their combinations
• Full compatibility with Gymnasium

Installation

Basic installation with full support of the environment functionalities (it requires Python 3.9+):

pip install "git+https://github.com/facebookresearch/HumEnv.git"

To use the MoCap and MoCapAndFall initalization schemes, you must prepare licensed datasets according to these instructions.

Full installation that includes all the benchmarking features:

pip install "humenv[bench] @ git+https://github.com/facebookresearch/HumEnv.git"

Quickstart

Once installed, you can create an environment using humenv.make_humenv which has a similar interface as gymnasium.make_vec. Here is a simple example:

from humenv import make_humenv
env, _ = make_humenv()
observation, info = env.reset()
frames = [env.render()]
for i in range(60):
    observation, reward, terminated, truncated, info = env.step(env.action_space.sample())
    frames.append(env.render())
# render frames at 30fps

More examples are available in the tutorial.

Citation

@article{tirinzoni2024metamotivo,
  title={Zero-shot Whole-Body Humanoid Control via Behavioral Foundation Models},
  author={Tirinzoni, Andrea and Touati, Ahmed and Farebrother, Jesse and Guzek, Mateusz and Kanervisto, Anssi and Xu, Yingchen and Lazaric, Alessandro and Pirotta, Matteo},
}

Acknowledgments

• SMPL and AMASS for the humanoid skeleton and motions used to initialise realistic positions for the tracking benchmark
• PHC for the data process and calculation of some Goal-reaching metrics
• SMPLSm for scripts used to process SMPL and AMASS datasets, and for humanoid processing utils
• smplx for removing chumpy dependency
• MuJoCo for the backend simulation engine
• Gymnasium for the API

License

Humenv is licensed under the CC BY-NC 4.0 license. See LICENSE for details.