A long-standing engineering problem, the control of soft robots is difficult because of their highly non-linear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, a neural reservoir is employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across a set of challenging tasks, outperforming classic neural network approaches. Further, by implementing a spiking reservoir on neuromorphic hardware, energy efficiency is achieved, with nearly two-orders of magnitude improvement relative to standard CPUs, with implications for the on-board control of untethered, small-scale soft robots.
@article{arxiv.2503.09477,
title = {Neural reservoir control of a soft bio-hybrid arm},
author = {Noel Naughton and Arman Tekinalp and Keshav Shivam and Seung Hung Kim and Volodymyr Kindratenko and Mattia Gazzola},
journal= {arXiv preprint arXiv:2503.09477},
year = {2025}
}