  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.
