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 repre- sent 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.


Presented at:
EEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, November 3-8, 2013
Year:
2013
Keywords:
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 Record created 2013-08-02, last modified 2018-03-17

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