MoS-VLA: ワンショットスキル適応を可能にする視覚-言語-行動モデル
MoS-VLA: A Vision-Language-Action Model with One-Shot Skill Adaptation
ロボット操作方策を学習済み基底関数の線形結合として表現し、新しいタスクには1回の実演だけで勾配更新なしに適応できるVLAフレームワークを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Ruihan Zhao, Tyler Ingebrand, Sandeep Chinchali, Ufuk Topcu
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models trained on large robot datasets promise general-purpose, robust control across diverse domains and embodiments. However, existing approaches often fail out-of-the-box when deployed in novel environments, embodiments, or tasks. We introduce Mixture of Skills VLA (MoS-VLA), a framework that represents robot manipulation policies as linear combinations of a finite set of learned basis functions. During pretraining, MoS-VLA jointly learns these basis functions across datasets from the Open X-Embodiment project, producing a structured skill space. At test time, adapting to a new task requires only a single expert demonstration. The corresponding skill representation is then inferred via a lightweight convex optimization problem that minimizes the L1 action error, without requiring gradient updates. This gradient-free adaptation incurs minimal overhead while enabling rapid instantiation of new skills. Empirically, MoS-VLA achieves lower action-prediction error on five out of five unseen datasets and succeeds in both simulation and real-robot tasks where a pretrained VLA model fails outright. Project page: mos-vla.github.io/
