SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion with Soft Snake Robots

TL;DR

SpikingSoft introduces a biologically inspired neural controller for soft snake robots, enabling adaptive locomotion through adjustable thresholds and reinforcement learning, resulting in improved success rates and smoother movements.

Contribution

The paper presents a novel Double Threshold Spiking neuron model that naturally integrates with reinforcement learning to control soft snake robots efficiently.

Findings

21.6% increase in success rate

29% reduction in time to reach target

smoother movements compared to baseline controllers

Abstract

Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we introduce the Double Threshold Spiking neuron model with adjustable thresholds to generate varied output patterns. This neuron model can excite the natural dynamics of soft robotic snakes, and it enables distinct movements, such as turning or moving forward, by simply altering the neural thresholds. Finally, we demonstrate that our approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning. The high-level agent only needs to adjust the two thresholds to generate complex movement patterns, thus strongly simplifying the learning of reactive locomotion. Simulation results demonstrate that the proposed architecture significantly enhances the performance of the soft snake robot, enabling it to achieve target objectives with a 21.6% increase in success rate, a 29% reduction in time to reach the target, and smoother movements compared to the vanilla reinforcement learning controllers or Central Pattern Generator controller acting in torque space.