Spiking neural state machine for gait frequency entrainment in a flexible modular robot

TL;DR

This paper introduces a neuromorphic control system using spiking neuron modules to generate and entrain gait cycles in a modular robot, demonstrating robustness and adaptability in locomotion control.

Contribution

The paper presents a novel modular neural architecture for closed-loop control of legged robots, enabling gait entrainment with minimal neuron modules.

Findings

Successful generation of crawling gait entrained to robot’s natural frequency

Robustness of the neural modules to parameter variations

Efficient control with only twelve neurons organized into four modules

Abstract

We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. Like its biological prototypes, this basic component is robust to parameter variation but can be modulated by external inputs. By combining these modules, we can construct a neural state machine capable of generating the cyclic or repetitive behaviors necessary for legged locomotion. A concrete case study for the approach is provided by a modular robot constructed from flexible plastic volumetric pixels, in which we produce a forward crawling gait entrained to the natural frequency of the robot by a minimal system of twelve neurons organized into four modules.