On-line learning of a feedback controller for quasi-passive-dynamic walking by a stochastic policy gradient method

A class of biped locomotion called passive dynamic walking (PDW) has been recognized to be efficient in energy consumption and a key to understand human walking. Although PDW is sensitive to the initial condition and disturbances, some studies of quasi-PDW, which introduces supplementary actuators, are reported to overcome the sensitivity. In this article, for realization of the quasi-PDW, an on-line learning scheme of a feedback controller based on a policy gradient reinforcement learning method is proposed. Computer simulations show that the parameter in a quasi-PDW controller is automatically tuned by our method utilizing the passivity of the robot dynamics. The obtained controller is robust against variations in the slope gradient to some extent.