Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration

Abstract-- An agent assisting humans in daily living activities can collaborate more effectively by anticipating upcoming tasks. Data-driven methods represent the state of the art in task anticipation, planning, and related problems, but these methods are resource-hungry and opaque. Our prior work introduced a proof of concept framework that used an LLM to anticipate 3 high-level tasks that served as goals for a classical planning system that computed a sequence of low-level actions for the agent to achieve these goals. This paper describes DaTAPlan, our framework that significantly extends our prior work toward human-robot collaboration. Specifically, DaTAPlan's planner computes actions for an agent and a human to collaboratively and jointly achieve the tasks anticipated by the LLM, and the agent automatically adapts to unexpected changes in human action outcomes and preferences. Figure 1: Illustration of "human-robot collaboration with anticipation": (a) agent anticipates (serving task) and collaborates with human, fetching juice from the fridge to the These methods are resource-hungry, i.e., need considerable This involves completing some high-level tasks, e.g., using classical planning to compute a sequence of finergranularity cooking breakfast and serving it at the table in Figure 1.