Transfer learning of gaits on a quadrupedal robot

Author:

Degrave Jonas1,Burm Michaël1,Kindermans Pieter-Jan2,Dambre Joni1,wyffels Francis1

Affiliation:

1. Electronics and Information Systems, Ghent University, Belgium

2. Department of Software Engineering and Theoretical Computer Science, TU Berlin, Germany

Abstract

Learning new gaits for compliant robots is a challenging multi-dimensional optimization task. Furthermore, to ensure optimal performance, the optimization process must be repeated for every variation in the environment, for example for every change in inclination of the terrain. This is unfortunately not possible using current approaches, since the time required for the optimization is simply too high. Hence, a sub-optimal gait is often used. The goal in this manuscript is to reduce the learning time of a particle swarm algorithm, such that the robot’s gaits can be optimized over a wide variety of terrains. To facilitate this, we use transfer learning by sharing knowledge about gaits between the different environments. Our findings indicate that using transfer learning new robust gaits can be discovered faster compared to traditional methods that learn a gait for each environment independently.

Publisher

SAGE Publications

Subject

Behavioral Neuroscience,Experimental and Cognitive Psychology

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