Efficient Robot Design with Multi-Objective Black-Box Optimization and Large Language Models

TL;DR

This paper introduces a novel approach that combines large language models with black-box optimization to improve the efficiency of robot body design, enabling more effective exploration of complex design spaces.

Contribution

The study presents a new method integrating LLMs with black-box optimization to enhance sampling efficiency in robot design, addressing limitations of traditional black-box methods.

Findings

LLMs improve sampling efficiency in black-box optimization.

The method enables better exploration of complex robot designs.

Discussion of the method's characteristics and limitations.

Abstract

Various methods for robot design optimization have been developed so far. These methods are diverse, ranging from numerical optimization to black-box optimization. While numerical optimization is fast, it is not suitable for cases involving complex structures or discrete values, leading to frequent use of black-box optimization instead. However, black-box optimization suffers from low sampling efficiency and takes considerable sampling iterations to obtain good solutions. In this study, we propose a method to enhance the efficiency of robot body design based on black-box optimization by utilizing large language models (LLMs). In parallel with the sampling process based on black-box optimization, sampling is performed using LLMs, which are provided with problem settings and extensive feedback. We demonstrate that this method enables more efficient exploration of design solutions and discuss its characteristics and limitations.