Collecting embodied interaction data at scale remains costly and difficult due to the limited accessibility of conventional interfaces. We present a gamified data collection framework based on Unity that combines procedural scene generation, VR-based humanoid robot control, automatic task evaluation, and trajectory logging. A trash pick-and-place task prototype is developed to validate the full workflow.Experimental results indicate that the collected demonstrations exhibit broad coverage of the state-action space, and that increasing task difficulty leads to higher motion intensity as well as more extensive exploration of the arm's workspace. The proposed framework demonstrates that game-oriented virtual environments can serve as an effective and extensible solution for embodied data collection.
@article{arxiv.2604.16903,
title = {Leveraging VR Robot Games to Facilitate Data Collection for Embodied Intelligence Tasks},
author = {Yihan Zhang and Ziyun Huang and Linqi Ye},
journal= {arXiv preprint arXiv:2604.16903},
year = {2026}
}