  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.
