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.

翻译：大规模收集具身交互数据依然成本高昂且困难，这归因于传统接口的有限可及性。我们提出一个基于Unity的游戏化数据收集框架，该框架结合了程序化场景生成、VR人形机器人控制、自动任务评估和轨迹记录等功能。为验证完整工作流程，开发了一个垃圾拾放任务原型。实验结果表明，收集到的演示动作覆盖了广泛的状态-动作空间，且任务难度的增加导致更高的运动强度以及机械臂工作空间的更大范围探索。所提出的框架表明，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。