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.