PUB - Publikationen an der Universität Bielefeld

We present an approach to dextrous robot grasping which combines a purely tactile-driven reactive algorithm with an implicit representation of grasp experience to yield an algorithm which can handle arbitrary, partially unknown grasp situations, i.e. vague object shape and position. 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.