Grasping in the Dark: Compliant Grasping using Shadow Dexterous Hand and BioTac Tactile Sensor
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When it comes to grasping and manipulating objects, the human hand is the benchmark based on which we design and model grasping strategies and algorithms. The task of imitating human hand in robotic end-effectors, especially in scenarios where visual input is limited or absent, is an extremely challenging one. In this paper we present an adaptive, compliant grasping strategy using only tactile feedback. The proposed algorithm can grasp objects of varying shapes, sizes and weights without having a priori knowledge of the objects. The proof of concept algorithm presented here uses classical control formulations for closed-loop grasping. The algorithm has been experimentally validated using a Shadow Dexterous Hand equipped with BioTac tactile sensors. We demonstrate the success of our grasping policies on a variety of objects, such as bottles, boxes and balls.
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