LLMにプロンプトを与えてロボットを歩かせる
Prompt a Robot to Walk with Large Language Models
物理環境から収集した少数のプロンプトを使い、タスク特化の微調整なしでLLMがロボットの低レベル制御コマンドを自己回帰的に生成し、歩行を実現する手法を提案した。
著者: Yen-Jen Wang, Bike Zhang, Jianyu Chen, Koushil Sreenath
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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/ .
