Training microrobots to swim by a large language model

• URL: http://arxiv.org/abs/2402.00044v1
• Date: Sun, 21 Jan 2024 12:18:59 GMT
• Title: Training microrobots to swim by a large language model
• Authors: Zhuoqun Xu and Lailai Zhu
• Abstract summary: We develop a minimal, unified prompt composed of only five sentences. The same prompt successfully guides two distinct articulated microrobots in mastering their signature strokes. Remarkably, our LLM-based decision-making strategy substantially surpasses a traditional reinforcement learning method in terms of training speed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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