Differential Mapping Spiking Neural Network for Sensor-Based Robot Control

TL;DR

This paper introduces a biologically inspired spiking neural network that models differential sensorimotor mappings for robot control, enabling real-time, noise-tolerant movement guidance using a novel tuning method.

Contribution

It presents a new SNN architecture with an intuitive tuning method for efficient robot control based on sensorimotor maps, validated through experiments.

Findings

Effective real-time robot control with noisy sensors

Reduced neuron count and training data with proposed tuning

Successful vision-guided robot experiments

Abstract

In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike-timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. As the main challenge to building an efficient SNN is to tune its parameters, we present an intuitive tuning method that considerably reduces the number of neurons and the amount of data required for training. Our proposed architecture represents a biologically plausible neural controller that is capable of handling noisy sensor readings to guide robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot.