通过三点接口的模块化设计实现可操控且反应式的抓取

中文摘要 AI 辅助

灵巧抓取需要决定抓取位置、到达目标并保持稳定接触。我们通过一个紧凑的三点接口将这些阶段连接起来，该接口将全局几何推理与局部接触控制分离。给定物体几何形状和可选的语言命令，我们的框架从预计算的抓取可供性热图中采样接触三元组。一个基于模型的反作用控制器跟踪物体、避免碰撞，并引导手部朝向选定的接触点。在最后几厘米内，一个强化学习（RL）策略利用本体感觉反馈来细化并稳定抓取，尽管存在到达和感知误差。它仅观察手指关节状态及其最近的动作，没有目标点、视觉观察或物体几何信息，因此单个策略可跨物体和抓取配置共享。在仿真中，我们将抓取并提升的成功率与挤压和端到端基线进行比较，表征到达收敛性，并演示抓取操控；在两个训练物体和一个未见物体上的硬件演示展示了完整流程。我们的模块化框架利用几何引导到达，利用局部反馈确保抓取稳固。

英文摘要

Dexterous grasping requires deciding where to grasp, reaching the target, and maintaining stable contact. We connect these stages through a compact three-point interface that separates global geometric reasoning from local contact control. Given object geometry and optional language commands, our framework samples contact triples from a precomputed grasp-affordance heatmap. A model-based reactive controller tracks the object, avoids collisions, and guides the hand toward the selected contacts. In the final centimeters, a Reinforcement Learning (RL) policy uses proprioceptive feedback to refine and stabilize the grasp despite reaching and perception errors. It observes only finger joint states and its recent actions, with no target points, visual observations, or object geometry, so a single policy is shared across objects and grasp configurations. In simulation, we compare grasp-and-lift success against squeeze and end-to-end baselines, characterize reaching convergence, and demonstrate grasp steering; hardware demonstrations on two training objects and one unseen object illustrate the full pipeline. Our modular framework uses geometry to guide the reach and local feedback to secure the grasp.

发表机构

• University of Michigan（密歇根大学）
• MIT（麻省理工学院）
• Yonsei University（延世大学）

机构由 AI 辅助整理，请以论文原文为准。