基于大语言模型训练微型机器人游泳
Training microrobots to swim by a large language model
AI总结：
本研究利用GPT-4大语言模型，通过仅含五句话的少样本统一提示词，训练两种铰接式微型机器人掌握物理学家提出的标志性泳姿，训练速度显著优于传统强化学习方法，并探讨了降低GPT-4使用成本的提示词设计要点。
摘要：
机器学习与人工智能近年来已成为设计和优化不同尺度机器人系统的主流范式。近期研究展示了大语言模型（large language models, LLMs）在工业控制[1]及引导足式步行机器人[2]领域的创新应用。本研究中，我们利用LLM模型GPT-4训练两种典型微型机器人在粘性流体中游泳。我们采用少样本学习方法，设计了仅由五句话组成的极简统一提示词。这一简洁的提示词成功引导两种不同的铰接式微型机器人——三连杆游泳机器人和三球游泳机器人——掌握其标志性泳姿。这些最初由物理学家提出的泳姿，如今被LLM有效解读并应用，使微型机器人得以规避微尺度运动固有的物理约束。值得注意的是，我们基于LLM的决策策略在训练速度上大幅超越传统强化学习方法。我们讨论了提示词设计的细节，特别强调了降低GPT-4使用成本的相关考量。
英文摘要：
Machine learning and artificial intelligence have recently represented a popular paradigm for designing and optimizing robotic systems across various scales. Recent studies have showcased the innovative application of large language models (LLMs) in industrial control [1] and in directing legged walking robots [2]. In this study, we utilize an LLM, GPT-4, to train two prototypical microrobots for swimming in viscous fluids. Adopting a few-shot learning approach, we develop a minimal, unified prompt composed of only five sentences. The same concise prompt successfully guides two distinct articulated microrobots -- the three-link swimmer and the three-sphere swimmer -- in mastering their signature strokes. These strokes, initially conceptualized by physicists, are now effectively interpreted and applied by the LLM, enabling the microrobots to circumvent the physical constraints inherent to micro-locomotion. Remarkably, our LLM-based decision-making strategy substantially surpasses a traditional reinforcement learning method in terms of training speed. We discuss the nuanced aspects of prompt design, particularly emphasizing the reduction of monetary expenses of using GPT-4.
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