Neural reservoir control of a soft bio-hybrid arm
This addresses the problem of controlling soft robots for applications like untethered, small-scale systems, with incremental advancements in efficiency and control methods.
The paper tackled the control of soft robots by employing a neural reservoir for dynamic control of a bio-hybrid arm, achieving nearly two-orders of magnitude energy efficiency improvement on neuromorphic hardware compared to standard CPUs.
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.