Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty Article Swipe

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Ziqin Yuan , Ruiqi Wang , Taehyeon Kim , Dezhong Zhao , Ike Obi , Byung‐Cheol Min ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.13824

Task allocation in multi-human multi-robot (MH-MR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA-HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic operational states. Additionally, we introduce an auxiliary state representation learning task to manage information uncertainty and enhance task execution. Through an extensive case study in large-scale environmental monitoring tasks, we demonstrate the benefits of our approach.

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Computer Science

Robot

Human–Robot Interaction

Human–Computer Interaction

Knowledge Management

Artificial Intelligence

Engineering

Systems Engineering

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Task (project management) Computer science Robot Human–robot interaction Human–computer interaction Knowledge management Artificial intelligence Engineering Systems engineering

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.13824
PDF: https://arxiv.org/pdf/2409.13824
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4403752478

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OpenAlex ID: https://openalex.org/W4403752478

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DOI: https://doi.org/10.48550/arxiv.2409.13824

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Title: Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-09-20

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Authors: Ziqin Yuan, Ruiqi Wang, Taehyeon Kim, Dezhong Zhao, Ike Obi, Byung‐Cheol Min

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Landing page: https://arxiv.org/abs/2409.13824

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PDF URL: https://arxiv.org/pdf/2409.13824

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OA status: green

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OA URL: https://arxiv.org/pdf/2409.13824

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Concepts: Task (project management), Computer science, Robot, Human–robot interaction, Human–computer interaction, Knowledge management, Artificial intelligence, Engineering, Systems engineering

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Cited by: 1

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Citations by year (recent): 2025: 1

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Related works (count): 10

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