A humanoid robot standing up through learning from demonstration using a multimodal reward function

Humans are known to manage postural movements in a very elegant manner. In the task of standing up from a chair, a humanoid robot can benefit from the variability of human demonstrations. In this paper we propose a novel method for humanoid robots to imitate a dynamic postural movement demonstrated by humans. Since the kinematics of human participants and the humanoid robot used in this experiment are different, we solve the correspondence problem by making comparisons in a common reward space defined by a multimodal reward function composed of balance and effort terms. We fitted a fully actuated triple inverted pendulum to model both human and robot. We used Differential Evolution to find the optimal articular trajectory that minimizes the Kullback-Leibler difference between the human's and robot's reward profile subject to constraints.