  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 spiking 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.
