Learning Robot Gait Stability using Neural Networks as Sensory Feedback Function for Central Pattern Generators

In this paper we present a framework to learn a model-free feedback controller for locomotion and balance control of a compliant quadruped robot walking on rough terrain. Having designed an open-loop gait encoded in a Central Pattern Generator (CPG), we use a neural network to represent sensory feedback inside the CPG dynamics. This neural network accepts sensory inputs from a gyroscope or a camera, and its weights are learned using Particle Swarm Optimization (unsupervised learning). We show with a simulated compliant quadruped robot that our controller can perform significantly better than the open-loop one on slopes and randomized height maps.


Editor(s):
Amato, N.
Published in:
2013 IEEE/RSJ International Conference On Intelligent Robots And Systems (Iros), 194-201
Presented at:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year:
2013
Publisher:
New York, Ieee
ISBN:
978-1-4673-6358-7
Laboratories:




 Record created 2014-06-02, last modified 2018-03-17


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