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Nonparametric representation of an approximated Poincaré map for learning biped locomotion

Nonparametric representation of an approximated Poincaré map for learning biped locomotion We propose approximating a Poincaré map of biped walking dynamics using Gaussian processes. We locally optimize parameters of a given biped walking controller based on the approximated Poincaré 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 Poincaré 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Nonparametric representation of an approximated Poincaré map for learning biped locomotion

Morimoto, Jun; Atkeson, Christopher
Autonomous Robots , Volume 27 (2) – Sep 1, 2009
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References (60)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Computer Science; Simulation and Modeling; Mechanical Engineering; Computer Imaging, Vision, Pattern Recognition and Graphics; Electrical Engineering; Control , Robotics, Mechatronics; Artificial Intelligence (incl. Robotics)
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-009-9133-z
Publisher site
See Article on Publisher Site

Abstract

We propose approximating a Poincaré map of biped walking dynamics using Gaussian processes. We locally optimize parameters of a given biped walking controller based on the approximated Poincaré 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 Poincaré 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.

Journal

Autonomous Robots – Springer Journals

Published: Sep 1, 2009

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