Robust Planning for Human-Robot Joint Tasks with Explicit Reasoning on Human Mental State
This work addresses planning for human-robot teams by improving belief alignment, but it is incremental as it builds on earlier approaches.
The paper tackles the human-aware task planning problem by modeling execution-time observability conventions and situation assessment to capture belief evolution and anticipate divergences, resulting in effective communication use and robustness for more realistic problems.
We consider the human-aware task planning problem where a human-robot team is given a shared task with a known objective to achieve. Recent approaches tackle it by modeling it as a team of independent, rational agents, where the robot plans for both agents' (shared) tasks. However, the robot knows that humans cannot be administered like artificial agents, so it emulates and predicts the human's decisions, actions, and reactions. Based on earlier approaches, we describe a novel approach to solve such problems, which models and uses execution-time observability conventions. Abstractly, this modeling is based on situation assessment, which helps our approach capture the evolution of individual agents' beliefs and anticipate belief divergences that arise in practice. It decides if and when belief alignment is needed and achieves it with communication. These changes improve the solver's performance: (a) communication is effectively used, and (b) robust for more realistic and challenging problems.