片手はもう一方を見る：動的環境でのサンプル効率的な両腕操作のための動的マルチエージェント協調
One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments
動的環境や両腕協調操作において、従来のマルチストリームポリシーが前提とする因果的独立性が崩れる問題を解決するため、反対側の腕を動的タスクパラメータとして扱う軽量なフレームワークDynaMACを提案し、静的デモから動的環境へのゼロショット一般化を実現した。
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著者: Jan Ole von Hartz, Abhinav Valada, Joschka Boedecker
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Multi-stream robot manipulation policies achieve unparalleled sample efficiency and generalization by modeling actions relative to environmental reference frames. However, existing approaches typically assume these frames to be strictly exogenous. This causal assumption collapses in dynamic settings, such as when a single robot arm manipulates a moving object or when two arms coordinate, where each arm effectively becomes part of the dynamic environment of the other. We propose DynaMAC, a lightweight, policy-agnostic framework that resolves this causal limitation while preserving the sample efficiency, computational speed, and flexibility of multi-stream policies, DynaMAC treats the opposite arm as a dynamic task parameter, thereby providing a unified formulation for dynamic manipulation and bimanual coordination without requiring an explicit leader-follower relationship. To rigorously evaluate these capabilities, we introduce DynaBench, a novel benchmark for robot manipulation in dynamic environments. Across both dynamic environments and bimanual manipulation tasks, DynaMAC outperforms leading probabilistic and generative baselines by over 35 percentage points while requiring 20 times fewer samples. Crucially, DynaMAC generalizes zero-shot from static demonstrations to dynamic environments, substantially simplifying data collection and establishing an elegant bridge toward human-robot collaboration.
