Prompt a Robot to Walk with Large Language Models

• URL: http://arxiv.org/abs/2309.09969v3
• Date: Tue, 15 Oct 2024 17:04:20 GMT
• Title: Prompt a Robot to Walk with Large Language Models
• Authors: Yen-Jen Wang, Bike Zhang, Jianyu Chen, Koushil Sreenath,
• Abstract summary: Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains. We introduce a novel paradigm in which we use few-shot prompts collected from the physical environment. Experiments across various robots and environments validate that our method can effectively prompt a robot to walk.
• Score: 18.214609570837403
• License:
• Abstract: 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/ .

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