NEAT and HyperNEAT based Design for Soft Actuator Controllers

• URL: http://arxiv.org/abs/2506.04698v1
• Date: Thu, 05 Jun 2025 07:20:16 GMT
• Title: NEAT and HyperNEAT based Design for Soft Actuator Controllers
• Authors: Hugo Alcaraz-Herrera, Michail-Antisthenis Tsompanas, Igor Balaz, Andrew Adamatzky,
• Abstract summary: This paper proposes an automated design method for the controller of soft actuators based on Neuroevolution.<n>Specifically, the suggested techniques employ Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT.<n>To test the robustness of the proposed methodologies, both high- and low-performing morphologies of soft actuators were utilized as testbeds.
• Score: 0.44998333629984877
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Since soft robotics are composed of compliant materials, they perform better than conventional rigid robotics in specific fields, such as medical applications. However, the field of soft robotics is fairly new, and the design process of their morphology and their controller strategies has not yet been thoroughly studied. Consequently, here, an automated design method for the controller of soft actuators based on Neuroevolution is proposed. Specifically, the suggested techniques employ Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT) to generate the synchronization profile of the components of a simulated soft actuator by employing Compositional Pattern Producing Networks (CPPNs). As a baseline methodology, a Standard Genetic Algorithm (SGA) was used. Moreover, to test the robustness of the proposed methodologies, both high- and low-performing morphologies of soft actuators were utilized as testbeds. Moreover, the use of an affluent and a more limited set of activation functions for the Neuroevolution targets was tested throughout the experiments. The results support the hypothesis that Neuroevolution based methodologies are more appropriate for designing controllers that align with both types of morphologies. In specific, NEAT performed better for all different scenarios tested and produced more simplistic networks that are easier to implement in real life applications.

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