Human Leading or Following Preferences: Effects on Human Perception of the Robot and the Human-Robot Collaboration

TL;DR

This study presents a task planning framework that incorporates human leadership and followership preferences to improve human-robot collaboration, enhancing team performance and human perception, with positive results from user studies.

Contribution

The paper introduces a proactive task planning framework that integrates human leadership styles into robot collaboration, demonstrating improved performance and perception.

Findings

Proactive task planning enhances collaboration effectiveness.

Participants' leadership styles influence collaboration dynamics.

The framework positively impacts human perception of robots.

Abstract

Achieving effective and seamless human-robot collaboration requires two key outcomes: enhanced team performance and fostering a positive human perception of both the robot and the collaboration. This paper investigates the capability of the proposed task planning framework to realize these objectives by integrating human leading/following preferences and performance into its task allocation and scheduling processes. We designed a collaborative scenario wherein the robot autonomously collaborates with participants. The outcomes of the user study indicate that the proactive task planning framework successfully attains the aforementioned goals. We also explore the impact of participants' leadership and followership styles on their collaboration. The results reveal intriguing relationships between these factors which warrant further investigation in future studies.