Training microrobots to swim by a large language model
Zhuoqun Xu Lailai Zhu
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.