There is huge demand for robots to work alongside humans in heterogeneous teams. To achieve a high degree of uidity, robots must be able to (1) recognize their human co-worker's intent, and (2) adapt to this intent accordingly, providing useful aid as a teammate. The literature to date has made great progress in these two areas { recognition and adaptation-but largely as separate research activities. In this work, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically within the same framework. We introduce Pike, an executive for humanrobot teams, that allows the robot to continuously and concurrently reason about what a human is doing as execution proceeds, as well as adapt appropriately. The result is a mixed-initiative execution where humans and robots interact uidly to complete task goals. Key to our approach is our task model: A contingent, temporally-exible team-plan with explicit choices for both the human and robot. This allows a single set of algorithms to find implicit constraints between sets of choices for the human and robot (as determined via causal link analysis and temporal reasoning), narrowing the possible decisions a rational human would take (hence achieving intent recognition) as well as the possible actions a robot could consistently take (hence achieving adaptation). Pike makes choices based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either agent). Innovations of this work include (1) a framework for concurrent intent recognition and adaptation for contingent, temporally-exible plans, (2) the generalization of causal links for contingent, temporally-exible plans along with related extraction algorithms, and (3) extensions to a state-of-The-Art dynamic execution system to utilize these causal links for decision making.
CITATION STYLE
Levine, S. J., & Williams, B. C. (2018). Watching and acting together: Concurrent plan recognition and adaptation for human-robot teams. Journal of Artificial Intelligence Research, 63, 281–359. https://doi.org/10.1613/jair.1.11243
Mendeley helps you to discover research relevant for your work.