Kinesthetic teaching of humanoid motion based on whole-body compliance control with interaction-aware balancing

In this work we present a framework for kinesthetic teaching and iterative refinement of whole body motions. For detection of external forces we apply a momentum based disturbance observer known from manipulator control to the floating-base model of a humanoid robot. These external forces are used as a trigger for implementing a compliant behavior at the interaction point and are integrated into a predictive balancing algorithm. For representation of the motion data, a hidden Markov model is used, which allows for an iterative update of the discrete motion states as well as a smooth generation of continuous motion data. Finally, we present an application of these algorithms on the humanoid robot TORO.