通过互连抓取：基于粗略物体模板的鲁棒闭合运动

中文摘要 AI 辅助

灵巧机器人手通常必须抓取形状、尺寸和位姿仅近似已知的物体。抓取规划器通常需要精确的物体模型或通过反馈来纠正误差，但闭合运动本身能够容忍多大的不准确性仍不清楚。为解决这一问题，我们设计了一个基于四个原则的运动规划器：物体的粗略模板、人类抓取类型、以物体为中心的交互，以及顺应性滑动接触而非预设接触点。本文介绍了该规划器，通过虚拟模型控制实现，并在Shadow灵巧手上进行了评估。在没有反馈的情况下，所规划的闭合运动容忍了约1厘米的尺寸误差以及数厘米和数十度的位姿误差，在27种测试条件中的25种下，其容忍范围优于最先进的数据驱动规划器。该规划器还成功抓取了80个日常物体中的82.5%，并在自主流程中成功完成。因此，鲁棒性可以设计到闭合运动本身中，而不仅仅依赖于反馈。该规划器为在不确定环境中实现可靠操作开辟了道路，我们将通过将其与物理手上的自适应反馈控制相结合来继续推进这一方向。

英文摘要

Dexterous robot hands must often grasp objects whose shape, size, and pose are known only approximately. Grasp planners typically require accurate object models or correct errors with feedback, but how much inaccuracy a closing motion can tolerate on its own remains unclear. To address this question, we designed a motion planner based on four principles: a coarse template of the object, human grasp types, an object-centric interaction, and compliant, sliding contacts instead of prescribed contact points. This paper presents the planner, implemented through virtual model control, and its evaluation on a Shadow Dexterous Hand. Without feedback, the planned closing motions tolerated size errors of about 1cm and pose errors of several centimeters and tens of degrees, a wider range than a state-of-the-art data-driven planner in 25 of 27 tested conditions. They also grasped 82.5% of 80 everyday objects and succeeded within an autonomous pipeline. Robustness can thus be designed into the closing motion itself, rather than left only to feedback. This planner opens a path toward reliable manipulation in uncertain settings, which we will pursue by combining it with adaptive feedback control on the physical hand.

发表机构

• University of Liège（列日大学）
• University of Cambridge（剑桥大学）

机构由 AI 辅助整理，请以论文原文为准。