外观
论文arXiv 2609.186501 个月前
从游戏玩法到策略：通过游戏化无机器人交互迈向可扩展的机器人数据收集
原文链接：From Gameplay to Policy: Towards Scalable Robot Data Collection via Gamified Robot-Free Interaction（arXiv 2609.18650 · 发布日期：2026-09-16）
From Gameplay to Policy: Towards Scalable Robot Data Collection via Gamified Robot-Free Interaction
提出VR游戏化Project Kitchen收集无硬件绑定操作数据，并用Game2Policy迁移到仿真和真机少样本策略验证。
核心要点
- 大规模多样化真机示教成本高，现有采集受硬件依赖或标注与行为多样性不足限制。
- Project Kitchen以VR第一视角游戏化采集目标导向操作，独立于具体机器人本体和硬件。
- Game2Policy从游戏轨迹提取接触点和子目标状态等本体不变可供性线索，并进行预训练与联合微调。
- 实验中Game2Policy在少样本设置下使仿真和真机平均成功率分别提升10.0和18.3点；平台和代码接收后发布。
验证范围与局限：验证范围包括仿真和真机少样本设置，摘要未说明具体任务、机器人数量或型号。
英文摘要 (Abstract)
Learning generalizable robot manipulation policies requires large-scale and diverse interaction data, yet collecting real-world demonstrations remains costly and difficult to scale. Existing approaches to data collection are either dependent on specific robot hardware that limits crowdsourcing and transferability, or suffer from incomplete annotation and limited behavioral diversity. Inspired by how games sustain long-term human engagement, we explore an alternative paradigm that turns data collection into an engaging gameplay experience and transfers the resulting human manipulation experience to real robots. We present Project Kitchen, a VR-based gamified egocentric data collection platform that elicits diverse, goal-directed manipulation while remaining independent of specific robot embodiments and hardware, making it applicable to broader and potentially large-scale deployment. To bridge the game-to-real gap, we further introduce Game2Policy, which extracts embodiment-invariant affordance cues, including contact points and sub-goal states, from gameplay trajectories. An affordance model is pre-trained on game-collected data and then jointly fine-tuned with downstream policies using only a handful of real-robot demonstrations. Experiments show that Game2Policy improves average success rates by 10.0 points in simulation and 18.3 points on real robots in the few-shot setting. User studies and quantitative analyses further show that Project Kitchen promotes diverse manipulation behaviors and provides an engaging data collection experience. These results demonstrate the potential of gamified virtual environments as a scalable source of manipulation knowledge. The platform and code will be released upon acceptance.
作者：Zheng Li, Liang Zhu, Junzhe Wang, Huayuan Chen, Ziyun Liu, Jiahang Cao, Xinyu Sheng, Pei Qu, Yufei Jia, Ximeng Zhang
来源
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