Respect Your Emotion: Human-Multi-Robot Teaming based on Regret Decision Model

TL;DR

This paper explores how incorporating regret-based decision models in human-multi-robot teaming enhances workload management and performance by mimicking human emotional responses like risk-seeking and risk-aversion.

Contribution

It introduces a regret decision model for robots in human-multi-robot teams and extends it for optimal request queuing, improving team efficiency.

Findings

Regret-based models induce human-like risk behaviors in robots.

Balancing workload and performance improves with regret-driven decision making.

Simulation results demonstrate benefits of emotion-inspired decision strategies.

Abstract

Often, when modeling human decision-making behaviors in the context of human-robot teaming, the emotion aspect of human is ignored. Nevertheless, the influence of emotion, in some cases, is not only undeniable but beneficial. This work studies the human-like characteristics brought by regret emotion in one-human-multi-robot teaming for the application of domain search. In such application, the task management load is outsourced to the robots to reduce the human's workload, freeing the human to do more important work. The regret decision model is first used by each robot for deciding whether to request human service, then is extended for optimally queuing the requests from multiple robots. For the movement of the robots in the domain search, we designed a path planning algorithm based on dynamic programming for each robot. The simulation shows that the human-like characteristics, namely, risk-seeking and risk-aversion, indeed bring some appealing effects for balancing the workload and performance in the human-multi-robot team.