Structured unsupervised kernel regression for closed-loop motion control

Transferring human skills to dextrous robots in an easy, fast and robust way is one of the key challenges that still have to be tackled in order to bring robots to our every-day life. However, many problems remain unsolved. In particular, researchers are seeking new paradigms along with efficient and robust task representations that facilitate adaptation to new contexts and provide a means to appropriately react to unforeseen situations. In this paper, we present a new method for robot behaviour synthesis, where intrinsic characteristics of `Structured UKR manifolds´ [13] are used to derive a closed-loop controller based on motion data obtained by the `Robot Skill Synthesis via Human Learning´ paradigm [10]. We apply the method to the task of swapping Chinese health balls with a real 16 DOF robotic hand. Our results indicate that the marriage of `Structured UKR manifolds´ with the `Robot Skill Synthesis via Human Learning´ paradigm yields an efficient way of realising a dexterous manipulation capability on real robots.