MoMo: 時空間アクションのトークン化によるロボット操作の動作モード制御
MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization
ロボット操作の実行スタイル（動作モード）をタスクから分離して学習し、指示に応じて動作の速さや滑らかさを変えられる2段階の模倣学習フレームワークMoMoを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Yuhan Hu, Hugues Thomas, Peide Huang, Mouli Sivapurapu, Benoit Landry, Arto Kivila
分類: cs.RO
原文アブストラクト
To operate effectively across diverse contexts, robots must not only perform manipulation tasks accurately but also adapt how their actions unfold to the task, object, and interaction setting. We ask whether this execution-level variation can be learned as a reusable behavioral factor shared across tasks. We present \textbf{MoMo}, a two-stage imitation-learning framework consisting of a spatiotemporal action tokenizer and a behavior-cloning transformer that takes task and a continuous motion-mode condition as inputs. Across six real-robot manipulation tasks, varying this condition produces steady, dynamic, and intermediate behaviors that human raters can distinguish and that differ in joint speed, acceleration, and end-effector approach pitch. On tasks demonstrated in only one mode, MoMo transfers the unseen requested mode while largely preserving task success. Together, these results provide evidence of compositional generalization to unseen task--mode combinations and show that motion mode can be reused across tasks to control how a manipulation skill is performed.
