Towards hardware Implementation of WTA for CPG-based control of a Spiking Robotic Arm

TL;DR

This paper presents a hardware implementation of a winner-take-all circuit within a spiking neural network to control a robotic arm in real-time, inspired by biological nervous systems and neuromorphic engineering.

Contribution

It introduces a novel WTA circuit in a spiking neural network processor for real-time robotic control, bridging neuroscience and robotics.

Findings

Feasibility of real-time robotic control with spiking neural circuits

Successful implementation of WTA in hardware for limb movement

Demonstrated brain-inspired control in a robotic arm

Abstract

Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in hardware for a deeper understanding and its possible application to solve complex problems in engineering and robotics. Central-Pattern-Generators (CPGs) are part of neuro-controllers, typically used at their last steps to produce rhythmic patterns for limbs movement. Different patterns and gaits typically compete through winner-take-all (WTA) circuits to produce the right movements. In this work we present a WTA circuit implemented in a Spiking-Neural-Network (SNN) processor to produce such patterns for controlling a robotic arm in real-time. The robot uses spike-based proportional-integrativederivative (SPID) controllers to keep a commanded joint position from the winner population of neurons of the WTA circuit. Experiments demonstrate the feasibility of robotic control with spiking circuits following brain-inspiration.