A Study of Demonstration-Based Learning of Upper-Body Motions in the Context of Robot-Assisted Therapy

In therapeutic scenarios, robots are sometimes used for imitation activities in which the robot demonstrates a motion and the individual under therapy needs to repeat it. To allow incorporating new types of motions in such activities, the robot should have an ability to learn motions by observing demonstrations from a human, such as a therapist. In this paper, we investigate an approach for acquiring motions from skeleton observations of a human, which are collected by a robot-centric RGB-D camera. The learning process from human body gestures to robot movements is done by mapping the joint angle positions to the robot's body, such that self-collisions of the end effector are prevented by re-estimating the angles in a safe angular position. We performed both a quantitative and a qualitative evaluation of the method, namely we (i) quantitatively evaluated the motion reproduction error of the procedure by performing a study with QTrobot in which the robot acquired different upper-body dance moves from multiple participants, and (ii) performed a qualitative user study to evaluate the robot's perceived reproduction. The quantitative evaluation demonstrates the method's overall feasibility, although the reproduction quality is affected by noise in the skeleton observations, while the qualitative evaluation suggests generally high satisfaction with the robot's motion, except for motions that are likely to lead to self-collisions and which were reproduced less accurately.