[2407.08626] RoboMorph: Evolving Robot Morphology using Large Language Models

Computer Science > Machine Learning arXiv:2407.08626 (cs) [Submitted on 11 Jul 2024 (v1), last revised 21 Mar 2026 (this version, v3)] Title:RoboMorph: Evolving Robot Morphology using Large Language Models Authors:Kevin Qiu, Władysław Pałucki, Krzysztof Ciebiera, Paweł Fijałkowski, Marek Cygan, Łukasz Kuciński View a PDF of the paper titled RoboMorph: Evolving Robot Morphology using Large Language Models, by Kevin Qiu and 5 other authors View PDF HTML (experimental) Abstract:We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. Each robot design is represented by a structured grammar, and we use LLMs to efficiently explore this design space. Traditionally, such exploration is time-consuming and computationally intensive. Using a best-shot prompting strategy combined with reinforcement learning (RL)-based control evaluation, RoboMorph iteratively refines robot designs within an evolutionary feedback loop. Across four terrain types, RoboMorph discovers diverse, terrain-specialized morphologies, including wheeled quadrupeds and hexapods, that match or outperform designs produced by Robogrammar's graph-search method. These results demonstrate that LLMs, when coupled with evolutionary selection, can serve as effective generative operators for automated robot design. Our project page and code are available at this https URL. Subjects: Machine Learning (cs.LG); Robotics (cs...