FP2：为机器人基础模型配备力控制

中文摘要 AI 辅助

机器人基础模型（RFM）在通用操作方面能力日益增强，但在接触丰富的环境中，可靠的物理交互仍然具有挑战性。我们提出FP2，一种轻量级的下游接口，为任务适配的RFM配备显式力控制，同时保留其动作生成能力。FP2采用动作调节分解：任务适配的RFM作为基础策略，负责任务级动作生成，而高频力控制策略仅专注于交互调节。为了将力调节与进行中的操作条件化，FP2压缩基础策略的上下文表示，并将其与力/力矩和历史本体感受信息结合，以预测结构化的力控制参数。我们使用四个RFM骨干网络在四个真实世界的接触丰富操作任务中评估FP2。FP2在任务性能和力调节质量上始终优于相应的基础策略，同时与代表性的力感知和力控制基线相比表现良好。消融研究进一步表明，基础策略上下文和物理反馈对于有效的力调节是互补的，而保留基础策略的动作生成既提高了效率，也改善了对新物体的泛化能力。项目网站：此http URL

英文摘要

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

发表机构

• FORTE Lab（FORTE实验室）
• Noematrix
• Flexiv
• SJTU（上海交通大学）
• UPenn（宾夕法尼亚大学）
• FDU（复旦大学）
• UIUC（伊利诺伊大学厄巴纳-香槟分校）
• ZJU（浙江大学）
• SII

机构由 AI 辅助整理，请以论文原文为准。