Grasping Unknown Objects by Exploiting Complementarity with Robot Hand Geometry

Grasping unknown objects with multi-fingered hands is challenging due to incomplete information regarding scene geometry and the complicated control and planning of robot hands. We propose a method for grasping unknown objects with multi-fingered hands based on shape complementarity between the robot hand and the object. Taking as input a point cloud of the scene we locally perform shape completion and then we search for hand poses and finger configurations that optimize a local shape complementarity metric. We validate the proposed approach in MuJoCo physics engine. Our experiments show that the explicit consideration of shape complementarity of the hand leads to robust grasping of unknown objects.