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

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.

References in corpus (6)

• Multi-Task Trust Transfer for Human-Robot Interaction
• Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments
• Improving User Specifications for Robot Behavior through Active Preference Learning: Framework and Evaluation
• FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation
• How to be Helpful? Supportive Behaviors and Personalization for Human-Robot Collaboration
• Helpfulness as a Key Metric of Human-Robot Collaboration

Cited by in corpus (1)

• To Lead or to Follow? Adaptive Robot Task Planning in Human-Robot Collaboration