LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application

• URL: http://arxiv.org/abs/2406.13787v1
• Date: Wed, 19 Jun 2024 19:18:40 GMT
• Title: LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application
• Authors: Zhe Huang, John Pohovey, Ananya Yammanuru, Katherine Driggs-Campbell,
• Abstract summary: Large Language Models (LLM) and Vision Language Models (VLM) enable robots to ground natural language prompts into control actions. We propose Language-driven Intention Tracking (LIT) to model the human user's long-term behavior and to predict the next human intention to guide the robot for proactive collaboration.
• Score: 4.519544934630495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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