用大模型加速机器人设计，提升黑箱优化效率
Efficient Robot Design with Multi-Objective Black-Box Optimization and Large Language Models
- 结合黑箱优化与大模型并行采样，动态生成设计方案
- 通过大模型反馈实现更高效的设计空间探索，减少迭代次数
- 适合需要快速试错的机器人结构设计场景
目前已有多种机器人设计优化方法，涵盖数值优化与黑箱优化。数值优化虽快，但难以处理复杂结构或离散变量，因此黑箱优化更为常用。然而，黑箱优化存在采样效率低的问题，需大量迭代才能获得优质解。本文提出一种基于大语言模型（LLMs）的增强型黑箱优化方法，在黑箱优化采样过程中，同时利用大模型根据问题设定和丰富反馈进行采样。实验表明，该方法能更高效地探索设计空间，并讨论其特性与局限性。
原文摘要 · Abstract (English)
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.
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