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

Summary: Improving humanoid locomotive performance with learnt approximated
dynamics via Gaussian processes for regression
Jun Morimoto, Christopher G. Atkeson, Gen Endo, and Gordon Cheng
Abstract-- We propose to improve the locomotive perfor-
mance 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 robotic systems - due to the large
number of iterations required to acquire suitable policies. In
this study, we first approximated the dynamics by using data
from a real robot, and then applied the estimated dynamics in
RL in order to improve stepping and walking policies. Gaussian
processes were used to approximate the dynamics. By using
Gaussian processes, we could estimate a probability distribution
of a target function with a given covariance function. Thus,
RL can take the uncertainty of the approximated dynamics
into account throughout the learning process. We show that
we can improve stepping and walking policies by using a RL
method with the approximated models both in simulated and
real environments. Experimental validation on a real humanoid

  

Source: Atkeson, Christopher G. - Robotics Institute, School of Computer Science, Carnegie Mellon University

 

Collections: Computer Technologies and Information Sciences; Engineering