外观
论文arXiv 2610.0567723 天前
从单机器人示范中学习协调的视觉运动箱体推动
原文链接：Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations（arXiv 2610.05677 · 发布日期：2026-10-05）
Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations
研究用遥操作单机器人数据学习多机器人协调箱体推动，系统考察数据构建与轻量策略，并在最多40机器人实验中验证。
核心要点
- 多机器人模仿学习因集体示范难以采集，在无通信、机载去中心化部署中仍理解不足。
- 作者将主要依赖减少机器人间干扰的协调箱体推动作为测试平台，利用单机器人遥操作数据。
- 研究系统比较数据集创建策略和轻量级策略架构，探索由单体示范扩展到大规模协调。
- 最多40个机器人的实验表明，仅被动观察其他运行机器人难以获得有效协调，是多机器人研究瓶颈。
验证范围与局限：验证范围为最多40个机器人的箱体推动实验；摘要未说明仿真或真机。
英文摘要 (Abstract)
Multi-robot imitation learning, particularly in settings where visuomotor policies are deployed in a communication-free, onboard decentralised style, represents an attractive paradigm. However, its realisation remains insufficiently understood, largely due to the difficulty of collecting collective demonstrations, since a single operator cannot control many robots simultaneously. Meanwhile, unlike coupled collaborative manipulation, many coordinated tasks achieve system-wide efficiency primarily through minimising inter-robot interference. This structure motivates us to study whether data collected by a teleoperated single-robot can be leveraged for large-scale coordinated box-pushing as a testbed. We systematically investigate dataset creation strategies and lightweight policy architectures. In particular, experiments with up to 40 robots highlight the difficulty of acquiring effective coordination solely through passive observation of other operating robots, revealing a concrete bottleneck for multi-robot research.
作者：Ryusei Matsumoto, Mei Kinjo, Yoko Sasaki, Keisuke Okumura
来源
本页事实对应的公开页面，以原文为准。