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An important challenge in robotics is achieving robust performance in object grasping and manipulation while dealing with noise and uncertainty. This paper presents an approach for addressing the performance of dexterous grasping under shape uncertainty. In our approach, the uncertainty in the object’s shape is parameterized and incorporated as a constraint into grasp planning. The proposed approach is used to plan feasible hand configurations for realizing planned contacts using different robotic hands. A compliant finger closing scheme is devised by exploiting both the object’s shape uncertainty and tactile sensing at the fingertips. Experimental evaluation demonstrates that our method improves the performance of dexterous grasping under shape uncertainty.

We present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile, and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems such as changes in the weight of the object, slippage, and external disturbances. For this purpose, we introduce the Hierarchical Fingertip Space (HFTS) as a representation that enables optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. Experimental evaluation is conducted on an Allegro hand mounted on a Kuka LWR arm.