全手灵巧抓取的实时力调节
Real-Time Force Regulation for Whole-Hand Dexterous Grasping
- Massachusetts Institute of Technology（麻省理工学院）
- Seoul National University（首尔国立大学）
- Yonsei University（延世大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于几何接触估计和约束优化的实时力调节框架，用于全手灵巧抓取，在动态接触下提升抓取稳定性与恢复能力。
AI中文摘要：
鲁棒的灵巧抓取要求在接触点在整个手部交互演变时保持物理稳定性。预先计算的力分配在物体运动、建模误差或外部扰动下容易失效。本文提出了一个框架，用于对动态变化的全手接触进行实时力调节。我们的方法利用跟踪的物体模型和本体感觉，对所有手部连杆的接触进行几何估计，无需在这些接触点使用触觉传感。它反复重新计算期望的接触力分配，受摩擦约束、执行器限制以及由经典全肢体力量分析启发的执行一致性约束。我们将此力调节控制器与反应式到达集成，使手部能够获取抓取、在扰动下维持抓取，并在失去物体后重新抓取。无重力仿真实验表明，在受控扰动下，与固定分配和仅指尖执行相比，抓取保持能力得到改善，而27自由度手臂-手系统的真实实验表明，在人类施加的扰动下，随着接触点在整个手部演变，抓取维持和恢复得以实现。项目页面：此https URL
英文摘要：
Robust dexterous grasping requires maintaining physical stability despite contacts interactively evolving across the entire hand. A precomputed force distribution can easily fail under object motion, modeling errors, or external disturbances. In this paper, we present a framework for real-time force regulation over dynamically changing whole-hand contacts. Our method geometrically estimates contacts across all hand links using a tracked object model and proprioception, without requiring tactile sensing at those contacts. It repeatedly recomputes the desired contact-force distribution subject to friction constraints, actuator limits, and an actuation-consistency constraint motivated by classical whole-limb force analysis. We integrate this force-regulation controller with reactive reaching, enabling the hand to acquire a grasp, maintain it under disturbances, and regrasp after losing the object. Simulation experiments without gravity demonstrate improved grasp retention over fixed-allocation and fingertip-only execution under controlled perturbations, while real-world experiments on a 27-DoF arm-hand system demonstrate grasp maintenance and recovery under human-applied disturbances as contacts evolve across the whole hand. Project page: https://sangminkim-99.github.io/reactive-grasp-whole-hand/
