Get started
Installation
Set up the Python environment with
uv, configure Vulkan rendering,
and download few GLB scene assets.
Quickstart
Create and step a benchmark environment with
gym.make, switch between
the L0-L3 levels, and run an end-to-end baseline training & evaluation loop.
The four levels L0-L3
Understand the benchmark semantics: trajectory replay, object end-state imitation,
semantic task imitation, and affordance-adapted imitation.
Data
Tutorials
Domain randomization
Randomize the background textures with
random_background=True and
scale object-placement jitter by tuning the xyz[:, :2] += rand * 0.02
offset in your task's _initialize_episode.
Data collection
Generate scripted motion-planning trajectories, batch-collect all 50 tasks at four
levels, and convert H5 demonstrations into LeRobot-format datasets.
Baselines
Train and evaluate ACT, Diffusion Policy, VQ-BeT, UniSkill, XSkill, GR00T, RDT,
π0/π0.5 and OpenVLA on the paired human-robot dataset contract.
Creating a new task
Build new L0-L3 tabletop tasks from the environment template, load YCB / RoboTwin /
PartNet / Sketchfab assets, and write a scripted motion-planning solution.
Contributing
Contribution guide
Contribute new tasks, models, robots and assets through the design templates, and
request human/robot pairing for your accepted tasks.
Community
Discord, the WeChat group, the four contribution paths, and the pairing service —
plus how a pull request becomes a public Arena comparison.
Apply for evaluation
Request a simulation or real-robot evaluation of your policy, and see what a
reproducible submission package has to contain.
Citation
BibTeX
@misc{zhou2026imitatorgamebenchmarkingrobot,
title={The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction},
author={Xunzhe Zhou and Yiyang Cai and Fengyi Wang and Ran Ju and Hanxiang Ren and Ruizhe Liu and Yu Zhang and Qian Luo and Feng Chen and Pei Zhou and Yi Ma and Yanchao Yang},
year={2026},
eprint={2608.22301},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2608.22301},
}