Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped
This work addresses control complexity for compliant robots, offering a novel method for robotic locomotion, though it is incremental in applying existing learning paradigms to a new domain.
The paper tackled the problem of controlling compliant quadrupedal robots by using spiking neural networks as central pattern generators, achieving closed-loop control with predefined gait patterns, speed control, and gait transitions in simulation.
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.