単独実演から協調的視覚運動ボックス押し動作を学習する
Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations
1台のロボットの遠隔操作データのみを用いて、最大40台のロボットが通信なしで協調して箱を押す視覚運動方策を学習する手法を検討し、データセット構築と軽量方策アーキテクチャを体系的に調査した。
著者: Ryusei Matsumoto, Mei Kinjo, Yoko Sasaki, Keisuke Okumura
分類: cs.RO
原文アブストラクト
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.