PUB - Publikationen an der Universität Bielefeld

Striving for an autonomous self-exploration of robots to learn their own body schema, i.e. body shape and appearance, kinematic and dynamic parameters, association of tactile stimuli to specific body locations, etc., we developed a tactile-servoing feedback controller that allows a robot to continuously acquire self-touch information while sliding a fingertip across its own body. In this manner one can quickly acquire a large amount of training data representing the body shape. We compare three approaches to track the common contact point observed when one robot arm is touching the other in a bimanual setup: feedforward control, solely relying on a coarse CAD-based kinematics performs worst, a solely feedback-based controller typically lacks behind, and only the combination of both approaches yields satisfactory tracking results. As a first, preliminary application, we use this self-touch capability to calibrate the closed kinematic chain formed by both arms touching each other. The obtained homogeneous transform describing the relative mounting pose of both arms improves end-effector position estimations by a magnitude.