FP2: ロボット基盤モデルに力制御を付与する軽量インターフェース
FP2: Equipping Robotic Foundation Models with Force Control
タスク適応済みのロボット基盤モデルに、高頻度の力制御ポリシーを組み合わせて接触の多い操作を安定化させる軽量な下流インターフェースFP2を提案し、実機4タスクで性能と力調整の品質を改善した。
詳しい要約
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著者: Hongjie Fang, Shirun Tang, Junjian Hu, Shidong Zhang, Derek Zhang, Linhao Chen, Dehai Li, Mingyu Mei, Wanxi Liu, Cewu Lu, Shiquan Wang
分類: cs.RO
原文アブストラクト
Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in contact-rich settings. We present FP2, a lightweight downstream interface that equips task-adapted RFMs with explicit force control while preserving their action-generation capability. FP2 adopts an action-regulation decomposition: the task-adapted RFM serves as a foundation policy responsible for task-level action generation, while a high-frequency force control policy focuses solely on interaction regulation. To condition force regulation on the ongoing manipulation, FP2 compresses foundation-policy contextual representations and combines them with wrench and proprioceptive histories to predict structured force-control parameters. We evaluate FP2 with four RFM backbones across four real-world contact-rich manipulation tasks. FP2 consistently improves task performance and force regulation quality over the corresponding foundation policies, while comparing favorably with representative force-aware and force-control baselines. Ablations further show that foundation-policy context and physical feedback are complementary for effective force regulation, while preserving foundation-policy action generation improves both efficiency and novel-object generalization. Project website: http://force-policy.github.io/fp2