  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 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. We evaluate DaTAPlan
capabilities in a realistic simulation environment, demonstrating accurate task
anticipation, effective human-robot collaboration, and the ability to adapt to
unexpected changes. Project website: https://dataplan-hrc.github.io
