Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing frame- work. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spik- ing neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
Populations of spiking neurons for reservoir computing : closed loop control of a compliant quadruped
2019-01-01
COGNITIVE SYSTEMS RESEARCH ; ISSN: 1389-0417
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Self-organized adaptive legged locomotion in a compliant quadruped robot
British Library Online Contents | 2008
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