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.
- 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
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"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 30fpsMore examples are available in the tutorial.
@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},
}
- 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
Humenv is licensed under the CC BY-NC 4.0 license. See LICENSE for details.