外观
论文arXiv 2609.192281 个月前
通过互连抓取：基于粗略物体模板的鲁棒闭合动作
原文链接：Grasping by interconnection: robust closing motions from coarse object templates（arXiv 2609.19228 · 发布日期：2026-09-16）
Grasping by interconnection: robust closing motions from coarse object templates
面向仅有近似形状、尺寸和位姿的物体，提出基于粗模板与顺应滑动接触的闭合规划器，并在Shadow Dexterous Hand上验证。
核心要点
- 动机：灵巧手常需抓取形状、尺寸和位姿仅近似已知的物体，闭合动作自身容错性尚不清楚。
- 方法：结合粗略物体模板、人类抓取类型、物体中心交互，以及非预设接触点的顺应滑动接触。
- 实现：规划器通过virtual model control实现，在无反馈条件下生成抓取闭合动作。
- 结果：在25/27个条件中优于数据驱动规划器，并抓取80个日用品达82.5%成功率。
验证范围与局限：验证范围为Shadow Dexterous Hand上的无反馈闭合、80个日常物体抓取及自主流水线中的成功情况。
英文摘要 (Abstract)
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.
作者：Julien Vanderheyden, Guillaume Drion, Fulvio Forni, Pierre Sacré
来源
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