Neuromorphic control of a simulated shape-memory-alloy-driven multi-legged robot using a spiking neural network

We present spiking-neural-network (SNN) reinforcement learning for autonomous locomotion control of a starfish-inspired multi-legged soft robot driven by shape memory alloy (SMA) actuators. Such robots are usually driven open loop by pre-designed periodic signals, because coordinated motion is hard to hand-design at high degrees of freedom. We replace the actor of Twin Delayed Deep Deterministic Policy Gradient (TD3) with an SNN: a Spiking Actor Network.

The membrane potential, an internal SNN state, is reconstructed at update time from a short observation history through the current actor, rather than stored at collection time. To reduce the controller’s inference cost, we add a regularization term on the squared membrane potentials, encouraging actions from fewer spikes. In simulation the agent acquired the leg coordination needed to approach a target, and its firing patterns developed a periodicity matched to the SMA response.

The resulting gait gives continuous propulsion without excessive firing. Goal error matched an ANN-actor TD3 baseline with smaller spread across seeds, and reconstruction gave the lowest goal error of the membrane-state treatments. The regularization lowered the firing rate until the estimated inference energy fell below the ANN’s.

This work was supported by JSPS KAKENHI Grant Number JP25K07685. Department of Computer Science, Chiba Institute of Technology, Narashino, Chiba, 275–0016, Japan Graduate School of Computer Science, Chiba Institute of Technology, Narashino, Chiba, 275–0016, Japan The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Neuromorphic control of a simulated shape-memory-alloy-driven multi-legged robot using a spiking neural network. Sci Rep (2026). https://doi.org/10.1038/s41598-026-75794-5 DOI: https://doi.org/10.1038/s41598-026-75794-5