标题： 基于LM的软模块化机器人设计探索 显示英文标题

标题： On the Exploration of LM-Based Soft Modular Robot Design

摘要： 近年来，大型语言模型（LLMs）在建模现实世界知识和增强基于知识的生成任务方面展现出了有前景的能力。 本文进一步探索了利用LLMs辅助软模块化机器人设计的潜力，同时考虑用户指令和物理定律，以减少通常需要大量试错实验才能实现满足特定结构或任务需求的机器人设计的依赖性。 具体而言，我们将机器人设计过程形式化为序列生成任务，并发现LLMs能够捕捉用自然语言表达的关键要求，并在机器人的构造序列中反映出来。 简而言之，我们没有进行真实世界的实验来评估设计质量，而是利用仿真工具向生成模型提供反馈，从而允许迭代改进而无需大量的手动标注。 此外，我们引入了五个评价指标，从任务完成情况、遵循指令等多个角度评估机器人设计的质量，支持自动化的评价流程。 我们的模型在为具有单向和双向运动以及下楼梯能力的软模块化机器人设计的评估中表现良好，凸显了使用自然语言和LLMs进行机器人设计的潜力。 然而，我们也观察到一些局限性，这表明了进一步改进的方向。 显示英文摘要

摘要： Recent large language models (LLMs) have demonstrated promising capabilities in modeling real-world knowledge and enhancing knowledge-based generation tasks. In this paper, we further explore the potential of using LLMs to aid in the design of soft modular robots, taking into account both user instructions and physical laws, to reduce the reliance on extensive trial-and-error experiments typically needed to achieve robot designs that meet specific structural or task requirements. Specifically, we formulate the robot design process as a sequence generation task and find that LLMs are able to capture key requirements expressed in natural language and reflect them in the construction sequences of robots. To simplify, rather than conducting real-world experiments to assess design quality, we utilize a simulation tool to provide feedback to the generative model, allowing for iterative improvements without requiring extensive human annotations. Furthermore, we introduce five evaluation metrics to assess the quality of robot designs from multiple angles including task completion and adherence to instructions, supporting an automatic evaluation process. Our model performs well in evaluations for designing soft modular robots with uni- and bi-directional locomotion and stair-descending capabilities, highlighting the potential of using natural language and LLMs for robot design. However, we also observe certain limitations that suggest areas for further improvement.