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

TL;DR

This paper presents an adaptive robot task planning framework that considers human preferences and performance to improve collaboration effectiveness, user experience, and task efficiency in human-robot teams.

Contribution

It introduces a proactive task allocation framework that balances human preferences and robot performance, validated through a user study in collaborative scenarios.

Findings

Framework improves team performance and human satisfaction

Successfully incorporates human leading/following preferences

Enhances positive perception of robot in collaboration

Abstract

Adaptive task planning is fundamental to ensuring effective and seamless human-robot collaboration. This paper introduces a robot task planning framework that takes into account both human leading/following preferences and performance, specifically focusing on task allocation and scheduling in collaborative settings. We present a proactive task allocation approach with three primary objectives: enhancing team performance, incorporating human preferences, and upholding a positive human perception of the robot and the collaborative experience. Through a user study, involving an autonomous mobile manipulator robot working alongside participants in a collaborative scenario, we confirm that the task planning framework successfully attains all three intended goals, thereby contributing to the advancement of adaptive task planning in human-robot collaboration. This paper mainly focuses on the first two objectives, and we discuss the third objective, participants' perception of the robot, tasks, and collaboration in a companion paper.