使用双极限表面的双手内在操作
Bimanual In-hand Manipulation using Dual Limit Surfaces
AI总结：
本文提出基于双极限表面的双手内在操作建模与规划框架，利用两侧摩擦贴片接触交替滑动与粘着，实现稳定、精确的物体重定向与重抓取。
摘要：
手内物体操作是灵巧操作的一项重要能力。本文介绍了一个用于手内物体重构的建模与规划框架，重点关注机器人手掌（或手指）与物体之间的摩擦贴片接触。我们的方法利用物体两侧的两个协作贴片接触，通过在滑动与粘着运动之间交替，在机器人的抓取范围内迭代地重新定位物体。与以往依赖单点接触或对接触动力学施加限制性假设的方法不同，我们的框架对双摩擦贴片的复杂交互进行建模，从而能够更好地控制物体运动。我们开发了一种规划算法，用于计算可行的运动，以重新定向和重新抓取物体，同时避免意外滑移。我们在仿真和真实世界实验中展示了该方法的有效性，结果表明在各种物体几何形状下，物体稳定性和位姿精度均有显著提升。
英文摘要：
In-hand object manipulation is an important capability for dexterous manipulation. In this paper, we introduce a modeling and planning framework for in-hand object reconfiguration, focusing on frictional patch contacts between the robot's palms (or fingers) and the object. Our approach leverages two cooperative patch contacts on either side of the object to iteratively reposition it within the robot's grasp by alternating between sliding and sticking motions. Unlike previous methods that rely on single-point contacts or restrictive assumptions on contact dynamics, our framework models the complex interaction of dual frictional patches, allowing for greater control over object motion. We develop a planning algorithm that computes feasible motions to reorient and re-grasp objects without causing unintended slippage. We demonstrate the effectiveness of our approach in simulation and real-world experiments, showing significant improvements in object stability and pose accuracy across various object geometries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
