Generating Active Explicable Plans in Human-Robot Teaming

TL;DR

This paper introduces an active explicable planning method for human-robot teaming that models and predicts human expectations dynamically, leading to more efficient and understandable robot plans.

Contribution

It proposes a Bayesian approach to model and predict changing human beliefs, enabling robots to generate anticipatory and explicable plans in dynamic collaboration.

Findings

Active explicable plans are more efficient than static ones.

The approach successfully captures dynamic human belief changes.

Experiments show improved plan explicability and efficiency.

Abstract

Intelligent robots are redefining a multitude of critical domains but are still far from being fully capable of assisting human peers in day-to-day tasks. An important requirement of collaboration is for each teammate to maintain and respect an understanding of the others' expectations of itself. Lack of which may lead to serious issues such as loose coordination between teammates, reduced situation awareness, and ultimately teaming failures. Hence, it is important for robots to behave explicably by meeting the human's expectations. One of the challenges here is that the expectations of the human are often hidden and can change dynamically as the human interacts with the robot. However, existing approaches to generating explicable plans often assume that the human's expectations are known and static. In this paper, we propose the idea of active explicable planning to relax this assumption. We apply a Bayesian approach to model and predict dynamic human belief and expectations to make explicable planning more anticipatory. We hypothesize that active explicable plans can be more efficient and explicable at the same time, when compared to explicable plans generated by the existing methods. In our experimental evaluation, we verify that our approach generates more efficient explicable plans while successfully capturing the dynamic belief change of the human teammate.