接触の多い作業のための力推論を暗黙的に行うVLA
FR-VLA: Implicit Force Reasoning VLA for Contact-Rich Manipulation
視覚言語の文脈から力の関連度を推定し、必要なときだけ力情報を行動生成に取り込むVLAモデルを提案。接触の多い5タスクで最高の成功率を達成した。
著者: Jiaju He, Zhiyuan Yang, Ruyang Han, Jiahong Tang, Zhiping Lin, Haiyue Zhu
分類: cs.RO
原文アブストラクト
Integrating force awareness into Vision-Language-Action (VLA) models involves not merely encoding wrench measurements as an additional modality, but determining when and how strongly physical interaction should influence robot action generation. Existing VLAs that use force typically inject it through a fixed pathway, without explicitly determining when it is relevant to the current action. This calls for an intermediate force relevance mechanism inside the VLA, in which visual-language context estimates the relevance of physical feedback to the current task before force enters the action pathway. FR-VLA addresses this gap with a Vision-Language Force-Reasoning Model that uses learnable force query tokens to derive a force admission signal grounded in visual-language context, and uses this signal to modulate an external tokenized force pathway for action generation. In real-world experiments on five contact-rich tasks across two robotic platforms, FR-VLA achieves the highest mean success rate over six evaluated stages, and trajectory visualizations show that its force admission varies with the interaction phase.