Problem
Research questions and friction points this paper is trying to address.
grasping
inaccuracy
closing motion
object templates
feedback
Grasping by interconnection: robust closing motions from coarse object templates
🤖 AI Summary
研究通过设计基于粗略物体模板、人类抓取类型等原则的运动规划器，解决机器人手在不准确物体模型下抓取物体的问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
coarse object templates
compliant sliding contacts
virtual model control
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