Watching and Acting Together: Concurrent Plan Recognition and Adaptation for Human-Robot Teams

Author:

Levine Steven James,Williams Brian Charles

Abstract

There is huge demand for robots to work alongside humans in heterogeneous teams. To achieve a high degree of fluidity, 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 human-robot 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 fluidly to complete task goals.Key to our approach is our task model: a contingent, temporally-flexible 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-flexible plans, (2) the generalization of causal links for contingent, temporally-flexible 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.

Publisher

AI Access Foundation

Subject

Artificial Intelligence

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Risk-Bounded Online Team Interventions via Theory of Mind;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. Human-AI Teaming: Following the IMOI Framework;Lecture Notes in Computer Science;2024

4. Cyclic Action Graphs for goal recognition problems with inaccurately initialised fluents;Knowledge and Information Systems;2023-10-03

5. The Role of Adaptation in Collective Human–AI Teaming;Topics in Cognitive Science;2022-11-14

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