Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains. Recently, there has been escalating interest in deploying LLMs for robotics, aiming to harness the power of foundation models in real-world settings. However, this approach faces significant challenges, particularly in grounding these models in the physical world and in generating dynamic robot motions. To address these issues, we introduce a novel paradigm in which we use few-shot prompts collected from the physical environment, enabling the LLM to autoregressively generate low-level control commands for robots without task-specific fine-tuning. Experiments across various robots and environments validate that our method can effectively prompt a robot to walk. We thus illustrate how LLMs can proficiently function as low-level feedback controllers for dynamic motion control even in high-dimensional robotic systems. The project website and source code can be found at: https://prompt2walk.github.io/ .

翻译：大规模互联网数据预训练的大语言模型在各领域展现出卓越能力。近来，将大语言模型应用于机器人领域以利用基础模型在现实世界中的能力引起了广泛关注。然而，这种方法面临重大挑战，特别是将这些模型锚定于物理世界以及生成动态机器人运动。为解决这些问题，我们提出了一种新型范式：通过从物理环境中采集的少量样本提示，使大语言模型能够自回归地为机器人生成低级控制指令，而无需特定任务的微调。在多种机器人和环境中的实验验证表明，我们的方法能够有效提示机器人行走。我们因此阐明了大语言模型如何能够在高维机器人系统中胜任动态运动控制的低级反馈控制器。项目网站与源代码参见：https://prompt2walk.github.io/