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

2024-01-03

IEEE Transactions on robotics

AI Summary

To address the lack of adaptability in human-robot collaborative task planning—specifically, its inability to dynamically accommodate human leader/follower preferences and real-time performance—the paper proposes a dual-objective active task allocation framework integrating online preference modeling with multi-objective performance optimization. Methodologically, it introduces a user behavior–driven preference estimation model, jointly optimizing task completion time, human cognitive load, and preference alignment; a lightweight real-time optimization algorithm is designed and empirically validated on an autonomous mobile manipulator platform. The key contribution lies in the first integration of explicit, adaptive preference modeling into a closed-loop task scheduling mechanism, thereby balancing collaboration naturalness and system efficiency. User studies demonstrate statistically significant improvements: +23.6% in task completion efficiency, +31.2% in preference matching rate, and enhanced collaboration fluency (p < 0.01).

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Human-Robot Interaction
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Adaptive robot task planning for human-robot collaboration
• Incorporating human preferences in task allocation and scheduling
• Enhancing team performance and human perception in collaboration

Innovation

• Adaptive task planning for human-robot collaboration
• Proactive task allocation with human preferences
• Enhancing team performance in collaborative settings