Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks

TL;DR

This paper demonstrates how recurrent spiking neural networks can control scalable, low-cost trunk-like robotic arms with high precision, using a novel learning and routing approach for complex joint coordination.

Contribution

It introduces a new control method using recurrent spiking neural networks for large-scale, articulated robotic arms with a scalable design based on simple 3D-printed modules.

Findings

Effective control of up to 75 degrees of freedom

Near millimeter accuracy in movement execution

Successful learning of motor-pose correlations

Abstract

In the paper, we show how scalable, low-cost trunk-like robotic arms can be constructed using only basic 3D-printing equipment and simple electronics. The design is based on uniform, stackable joint modules with three degrees of freedom each. Moreover, we present an approach for controlling these robots with recurrent spiking neural networks. At first, a spiking forward model learns motor-pose correlations from movement observations. After training, intentions can be projected back through unrolled spike trains of the forward model essentially routing the intention-driven motor gradients towards the respective joints, which unfolds goal-direction navigation. We demonstrate that spiking neural networks can thus effectively control trunk-like robotic arms with up to 75 articulated degrees of freedom with near millimeter accuracy.