SOM-based experience representation for Dextrous Grasping

Abstract

We present an approach to dextrous robot grasping which combines a purely tactile-driven algorithm with an implicit representation of grasp experience to yield an algorithm which can handle arbitrary, partially unknown grasp situations. During the grasp movement, the obtained contact information is used to dynamically adapt the grasping control by targeting the best matching posture from the experience base. Thus, the robot recalls and actuates a grasp it already successfully performed in a similar tactile context. To efficiently represent the experience, we introduce the Grasp Manifold assuming that grasp postures form a smooth manifold in hand posture space. We present a simple way of providing approximations of Grasp Manifolds using Self-Organising Maps (SOMs) and study the properties of the represented grasp manifolds concerning their smoothness and robustness against clustered training data.