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.

翻译：迄今为止，已发展出多种机器人设计优化方法。这些方法多种多样，从数值优化到黑盒优化均有涉及。数值优化虽然速度快，但不适用于涉及复杂结构或离散值的情况，因此常转而使用黑盒优化。然而，黑盒优化存在采样效率低下的问题，需要相当多的采样迭代才能获得良好解。在本研究中，我们提出一种利用大语言模型（LLMs）来提升基于黑盒优化的机器人本体设计效率的方法。在与基于黑盒优化的采样过程并行的同时，利用大语言模型进行采样，并向其提供问题设定与大量反馈信息。我们证明该方法能够更高效地探索设计方案，并讨论了其特性与局限性。