Nonparametric representation of an approximated Poincar, map for learning biped locomotion

We propose approximating a Poincar, map of biped walking dynamics using Gaussian processes. We locally optimize parameters of a given biped walking controller based on the approximated Poincar, map. By using Gaussian processes, we can estimate a probability distribution of a target nonlinear function with a given covariance. Thus, an optimization method can take the uncertainty of approximated maps into account throughout the learning process. We use a reinforcement learning (RL) method as the optimization method. Although RL is a useful non-linear optimizer, it is usually difficult to apply RL to real robotic systems due to the large number of iterations required to acquire suitable policies. In this study, we first approximated the Poincar, map by using data from a real robot, and then applied RL using the estimated map in order to optimize stepping and walking policies. We show that we can improve stepping and walking policies both in simulated and real environments. Experimental validation on a humanoid robot of the approach is presented.