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: this https URL .

使用自然语言处理模型（LLMs）通过少量样本来实现机器人的动态运动控制，充分展示了机器人领域中大规模预训练模型的潜力。