Neural reservoir control of a soft bio-hybrid arm

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. Hyper-redundancy, underactuation, distributedness, and continuum in principle can be any dynamical system (31), integrates and mechanics are defining features of soft robots (artificial projects input data streams into a separable, high-dimensional or biological (1-8)), intrinsic to their compliant, elastic constitutive latent space that decomposes non-linear correlations. These traits are attractive in the pursuit of extreme dynamics are then sampled and recombined via linear maps reconfigurability, morphological adaptivity, delicacy and dexterity, into desired computations. Modelbased different tasks while running on the same reservoir, and can be controllers have proven effective in quasi-static settings, matched with specialized hardware (e.g., neuromorphic systems but lack accuracy when inertial effects become significant and for energy efficiency (33, 34)) or'wetware' (neural tissue used typically rely on simplifying assumptions that may overlook as bio-hybrid reservoir (35)).