LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application
This addresses the need for more efficient and proactive collaboration in human-robot systems, though it appears incremental as it builds on existing LLM/VLM capabilities.
The paper tackles the problem of excessive prompting in long-horizon human-robot collaboration by proposing LIT, which uses LLMs and VLMs to model human behavior and predict intentions, resulting in smooth coordination in cooking tasks.
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.