Leveraging VR Robot Games to Facilitate Data Collection for Embodied Intelligence Tasks

TL;DR

This paper introduces a VR-based gamified framework using Unity for scalable data collection in embodied intelligence tasks, validated through a trash pick-and-place prototype.

Contribution

It presents a novel VR gamified system combining procedural scene generation, automatic evaluation, and trajectory logging for efficient embodied data collection.

Findings

Collected demonstrations cover broad state-action space

Higher task difficulty increases motion intensity

Virtual environments are effective for embodied data collection

Abstract

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.