We present a Bayesian approach to calibrating the hand-eye kinematics of an anthropomorphic robot. In our approach, the robot perceives the pose of its end-effector with its head-mounted camera through visual markers attached to its end-effector. It collects training observations at several configurations of its 7-DoF arm and 2-DoF neck which are subsequently used for an optimization in a batch process. We tune Denavit-Hartenberg parameters and joint gear reductions as a minimal representation of the rigid kinematic chain. In order to handle the uncertainties of marker pose estimates and joint position measurements, we use a maximum a posteriori formulation that allows for incorporating prior model knowledge. This way, a multitude of parameters can be optimized from only few observations. We demonstrate our approach in simulation experiments and with a real robot and provide indepth experimental analysis of our optimization approach.
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