Imitation learning, also known as Programming by Demonstration, allows a non-expert user to teach complex skills to a robot. While so far researchers focused on abstracting kinematic relations, only little attention has been paid to force information. In this work we study imitation learning of human grasping skills from motion and force data. For this purpose a teleoperation system is realized that allows a human to control a simulated robotic hand and to grasp objects in a virtual environment. Haptic rendering algorithms are implemented to calculate interaction forces that occur when touching the virtual object. While learning of fingertip interaction forces is shown to result in physical inconsistency compared to the demonstrations, we show that learning of internal tensions leads to stable reproductions of the demonstrated grasping skill. Obtained results further indicate an enlarged generalisation capability of grasping skills learnt on the basis of motion and force data compared to grasping skills that encode kinematic relations only.

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Ball dribbling is a central element of basketball. One main challenge for realizing basketball robots is to stabilize periodic motion of the ball. The task is nontrivial due to the discrete-continuous nature of the corresponding dynamics. This paper proposes to add an elastic element to the manipulator so the ball can be controlled in a continuous-time phase instead of an intermittent contact. Optimal catching and pushing trajectories are planned for the underactuated system based on the virtual holonomic constraints approach. First experimental studies are presented to evaluate the approach. A. Shiriaev is also with the