2023/09/25 by Jie Luo, Luo, Jie, Jakub M. Tomczak +5 · 1 citation
Computer Science · Engineering · #3D Printing in Biomedical Research #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Modular Robots and Swarm Intelligence #Neural and Evolutionary Computing (cs.NE) #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2309.13908
openalex publication_date 2023/09/25 · openalex created_date 2023/09/27 · openalex updated_date 2026/07/28
The main question this paper addresses is: What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in advance? Our interest is rooted in the context of morphologically evolving modular robots, but the question is also relevant in general, for system designers interested in widely applicable solutions. We perform an experimental comparison of three controller-and-learner combinations: one approach where controllers are based on modelling animal locomotion (Central Pattern Generators, CPG) and the learner is an evolutionary algorithm, a completely different method using Reinforcement Learning (RL) with a neural network controller architecture, and a combination `in-between' where controllers are neural networks and the learner is an evolutionary algorithm. We apply these three combinations to a test suite of modular robots and compare their efficacy, efficiency, and robustness. Surprisingly, the usual CPG-based and RL-based options are outperformed by the in-between combination that is more robust and efficient than the other two setups.