Recently, researchers have explored control methods that embrace nonlinear dynamic coupling instead of suppressing it. Such designs leverage dynamical coupling for communication between different parts of the robot. Morphological communication refers to when those dynamics can be used as an emergent data bus to facilitate coordination among independent controller modules within the same robot. Previous research with tensegrity-based robot designs has shown that evolutionary learning models that evolve spiking neural networks (SNN) as robot control mechanisms are effective for controlling non-rigid robots. Our own research explores the emergence of morphological communication in an SNN-based simulated soft robot in theEvoGym environment.
@article{arxiv.2508.19920,
title = {Walk the Robot: Exploring Soft Robotic Morphological Communication driven by Spiking Neural Networks},
author = {Matthew Meek and Guy Tallent and Thomas Breimer and James Gaskell and Abhay Kashyap and Atharv Tekurkar and Jonathan Fischman and Luodi Wang and Viet-Dung Nguyen and John Rieffel},
journal= {arXiv preprint arXiv:2508.19920},
year = {2025}
}