Leveraging VR Robot Games to Facilitate Data Collection for Embodied Intelligence Tasks
For researchers in embodied AI, this provides a more accessible and scalable data collection method compared to physical robot interfaces.
The paper presents a gamified VR-based framework for collecting embodied interaction data, demonstrating that it yields broad state-action coverage and increased exploration with task difficulty.
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.