A Feedback Sensor Based on Spiking Neural Networks for Real-Time Robot Adaption

In recent years, it has been observed how the locomotion systems of vertebrate animals serve as inspiration to enhance the performance of robotic systems. These animal systems are characterized by their ability to adapt to environmental changes detected by their biological sensors. With this model in mind, our objective is to replicate this adaptability in robotics through the use of a Central Pattern Generator (CPG). We present an advanced robotic system based on Spiking Neural Networks (SNNs), implemented in both Spinnaker and Field Programmable Gate Arrays (FPGAs). This system is capable of modifying its locomotion pattern based on information provided by Force Sensitive Resistors (FSRs). Our experiments demonstrate that this platform can adapt in real-time to the terrain in which it operates.