发表机构
Technical University of Munich; Resense GmbH（慕尼黑工业大学; Resense有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对粗位置控制夹爪，提出几何感知的力/力矩接触估计与自适应导纳控制，实现未知易碎物体的安全稳定抓取，实验验证防滑且施加最小力。
AI 中文摘要
机器人越来越多地应用于非结构化环境中。它们在不损坏未知物体的情况下安全抓取物体的需求变得至关重要。人类通过感知并快速响应来调整抓取力来实现这一点。类似地，机器人的有效抓取获取需要柔顺的交互策略，该策略能够适应不确定的物体属性，并在操作过程中调整任何不稳定性。我们提出了一种针对粗位置控制夹爪的基于几何感知的力/力矩接触估计方法，并结合自适应导纳控制器以实现安全抓取获取。期望接触力在线估计，以与未知属性的物体保持稳定接触。这实现了柔顺且稳定的抓取，同时避免了过大的力。使用不同尺寸、形状、刚度和重量的物体进行的实验表明，所提出的算法不仅能防止滑动，还能施加最小的力来安全抓取物体，而不会造成过度变形。
英文摘要
Robots are increasingly used in unstructured environments. The need for them to safely grasp unknown objects without damaging them becomes crucial. Humans achieve this by sensing and quickly responding by adjusting their grasping force. Similarly, effective grasp acquisition in robots requires compliant interaction strategies that can adapt to uncertain object properties and adjust to any instabilities during manipulation. We present a geometry-aware force/torque-based contact estimation method for a coarse position-controlled gripper, combined with an adaptive admittance controller for safe grasp acquisition. The desired contact forces are estimated online to keep stable contact with objects of unknown properties. This enables compliant and stable grasps while avoiding excessive forces. Experiments with objects of different sizes, shapes, stiffnesses, and weights show that the proposed algorithm not only prevents slippage but also applies minimal force to safely grasp an object without causing excessive deformation.
CommentsAccepted for IEEE IROS 2026 publication
