TAO-Force: 接触の多いマニピュレーションのための力認識知覚と高速・低速制御の統合
TAO-Force: Unifying Force-Aware Perception and Fast-Slow Control for Contact-Rich Manipulation
力フィードバックを視覚言語モデルに注入し、接触時のみ高速なアドミタンス制御に切り替える枠組みを提案し、接触の多い操作タスクでの有効性を示した。
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著者: Bohan Gan, Xuanzhang Wen, Yongsheng Zhao, Baoping Cheng, Wenhe Jia, Ye Wang, Gongxin Yao, Han Gao, Jingyao Tang, Lei Zhao, Ji Ge
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models have demonstrated strong performance across diverse robotic manipulation tasks, yet their predominantly vision-centric perception and position-controlled execution remain insufficient for contact-rich manipulation. Visual observations alone often provide limited evidence of contact onset and interaction magnitude, while position-control policies cannot respond compliantly to rapidly changing contact dynamics. To bridge both the perception and control gaps, we propose TAO-Force, a force-conditioned VLA framework that combines force-aware policy learning with contact-regulated execution. For force-aware perception, TAO-Force introduces Force-conditioned Feature-wise Linear Modulation (F-FiLM) to inject encoded force feedback into the representations of a frozen pretrained visual-language backbone while preserving its semantic priors. For responsive control, it employs a contact-gated fast-slow architecture, with a slow position-control branch tracking nominal trajectories during non-contact phases and a fast admittance-control branch regulating physical interaction during contact phases. Detailed analyses on a force-perception task and real-world evaluations across four contact-rich manipulation tasks validate the effectiveness and robustness of TAO-Force.