用游戏化虚拟环境收集机器人交互数据，提升效率与多样性。
Leveraging VR Robot Games to Facilitate Data Collection for Embodied Intelligence Tasks
- 基于Unity构建游戏化框架，支持自动任务评估与轨迹记录
- 实验显示数据覆盖状态-动作空间广，难度越高探索越充分
- 适合需要大量机器人交互数据的研究者快速获取高质量数据
由于传统接口可及性有限，大规模获取具身交互数据仍成本高昂且困难。本文提出一种基于Unity的游戏化数据收集框架，融合程序化场景生成、基于VR的人形机器人控制、自动任务评估与轨迹日志功能。以垃圾拾取放置任务原型验证全流程。实验结果表明，所收集示范覆盖了广泛的状态-动作空间；任务难度增加时，运动强度上升，机械臂工作空间探索更全面。该框架证明，面向游戏的虚拟环境可作为具身数据收集的有效且可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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