RoboMorph: Evolving Robot Morphology using Large Language Models

2024-07-11

arXiv.org

AI Summary

Modular robot design is time-consuming and computationally expensive due to the combinatorial complexity of morphology-space exploration. Method: This paper proposes an LLM-driven morphological evolution paradigm: (i) modeling robot topologies via grammar-based representations; (ii) leveraging large language models (LLMs) for semantic parsing and evolutionary guidance; (iii) introducing context-aware best-shot prompting to embed semantic reasoning into topology optimization; and (iv) establishing a “design–control” closed-loop feedback system that jointly optimizes morphology and control via evolutionary algorithms and reinforcement learning for multi-terrain locomotion. Results: Experiments demonstrate autonomous generation of diverse, nontrivial terrain-adaptive morphologies, with consistent improvement in morphological performance across generations. This work constitutes the first empirical validation of LLMs for navigating high-dimensional, structured engineering design spaces, establishing a novel pathway toward intelligent, autonomous design systems.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. In this framework, we represent each robot design as a grammar and leverage the capabilities of LLMs to navigate the extensive robot design space, which is traditionally time-consuming and computationally demanding. By introducing a best-shot prompting technique and a reinforcement learning-based control algorithm, RoboMorph iteratively improves robot designs through feedback loops. Experimental results demonstrate that RoboMorph successfully generates nontrivial robots optimized for different terrains while showcasing improvements in robot morphology over successive evolutions. Our approach highlights the potential of using LLMs for data-driven, modular robot design, providing a promising methodology that can be extended to other domains with similar design frameworks.

Problem

• Automates modular robot design using LLMs and evolution
• Optimizes robot morphology for diverse terrains efficiently
• Reduces computational demands in robot design exploration

Innovation

• LLMs and evolutionary algorithms optimize robot designs
• Grammar-based design representation navigates complex space
• Reinforcement learning controls iterative design improvements