Walk the Robot: Exploring Soft Robotic Morphological Communication driven by Spiking Neural Networks

2025-08-27

AI Summary

Distributed coordination among controller modules in soft robots remains challenging due to the absence of robust, scalable communication mechanisms. Method: We propose a morphology-driven communication paradigm leveraging spiking neural networks (SNNs) to exploit the robot’s intrinsic nonlinear dynamics as an implicit “morphological data bus.” Rather than relying on explicit signal transmission, inter-module coordination emerges spontaneously through dynamic coupling of the embodied soft structure. We jointly optimize SNN controllers and soft morphology using the EvoGym simulator and an evolutionary learning framework. Contribution/Results: Our approach significantly enhances locomotion robustness and environmental adaptability without centralized control or explicit communication protocols. Experiments demonstrate successful self-organized multi-module coordination. This work provides the first systematic validation that morphological dynamics—under SNN control—can serve as an evolvable, self-sustaining communication medium, establishing a novel paradigm for embodied intelligence and morphological computation.

Technology Category

Cognitive Modeling & Cognitive Systems: Neural Spike CodingIntelligent Robots: Embodied AISearch and Optimization: Evolutionary Computation

Application Category

Web Mining and Content Analysis: Models for Web evolutionResponsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Exploring soft robot control using spiking neural networks
• Leveraging morphological communication for robot coordination
• Studying emergent communication in simulated soft robots

Innovation

• Spiking Neural Networks control soft robots
• Morphological communication enables emergent coordination
• Evolutionary learning optimizes nonlinear dynamic coupling