Interactive Plan Explicability in Human-Robot Teaming

TL;DR

This paper introduces Interactive Plan Explicability, a new measure for human-robot cooperation that considers mutual influence in joint plans, leading to more aligned and efficient teamwork.

Contribution

It extends plan explicability to interactive settings, enabling robots to generate plans that better match human expectations during collaboration.

Findings

Plans generated with Interactive Plan Explicability are comparable to human plans.

Our approach outperforms traditional planning without explicability considerations.

Results suggest improved efficiency and alignment in human-robot teaming.

Abstract

Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware that its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot cooperation with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where human and robot behaviors can influence each other. We term this new measure as Interactive Plan Explicability. We compare the joint plan generated with the consideration of this measure using the fast forward planner (FF) with the plan created by FF without such consideration, as well as the plan created with actual human subjects. Results indicate that the explicability score of plans generated by our algorithm is comparable to the human plan, and better than the plan created by FF without considering the measure, implying that the plans created by our algorithms align better with expected joint plans of the human during execution. This can lead to more efficient collaboration in practice.