Improving humanoid locomotive performance with learnt approximated dynamics via Gaussian processes for regression

摘要

We propose to improve the locomotive performance of humanoid robots by using approximated biped stepping and walking dynamics with reinforcement learning (RL). Although RL is a useful non-linear optimizer, it is usually difficult to apply RL to real robot