LIT: 大規模言語モデルによる意図追跡で実現する先回り型人ロボット協調 — ロボット副料理長アプリケーション
LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application
LLMとVLMで人間の長期的な行動と次の意図を予測し、指示待ちではなく先回りして協力するロボットを実現。料理タスクで人間との円滑な共同作業を実証した。
著者: Zhe Huang, John Pohovey, Ananya Yammanuru, Katherine Driggs-Campbell
分類: cs.RO, cs.CV
原文アブストラクト
Large Language Models (LLM) and Vision Language Models (VLM) enable robots to ground natural language prompts into control actions to achieve tasks in an open world. However, when applied to a long-horizon collaborative task, this formulation results in excessive prompting for initiating or clarifying robot actions at every step of the task. We propose Language-driven Intention Tracking (LIT), leveraging LLMs and VLMs to model the human user's long-term behavior and to predict the next human intention to guide the robot for proactive collaboration. We demonstrate smooth coordination between a LIT-based collaborative robot and the human user in collaborative cooking tasks.
