基盤モデルの実世界ロボット応用：レビュー
Real-World Robot Applications of Foundation Models: A Review
LLMやVLMなどの基盤モデルを実ロボットシステムの知覚・運動計画・制御にどう組み込むかを、入出力関係の観点から整理したレビュー。
著者: Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella, Jiaxian Guo, Chris Paxton, Andy Zeng
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), trained on extensive data, facilitate flexible application across different tasks and modalities. Their impact spans various fields, including healthcare, education, and robotics. This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems. The summary encompasses the perspective of input-output relationships in foundation models, as well as their role in perception, motion planning, and control within the field of robotics. This paper concludes with a discussion of future challenges and implications for practical robot applications.
