Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks
This enables scalable, low-cost robotic arm control for applications requiring many joints, though it appears incremental in combining existing spiking neural network techniques with a specific robot design.
The paper tackles the problem of controlling highly articulated trunk-like robotic arms by developing a control approach using recurrent spiking neural networks, achieving near millimeter accuracy for arms with up to 75 degrees of freedom.
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.