Multi-Robot Task Allocation

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Multi-robot task allocation studies how teams divide tasks so robots with different abilities can complete missions efficiently, safely, and reliably. Common approaches include auctions and consensus for distributed assignment, matching and mathematical optimization for structured problems, and reinforcement learning or graph-based policies for dynamic settings. In many applications, allocation is coupled with routing, path planning, scheduling, communication, and constraints such as battery life or shared resources. A central challenge is balancing solution quality with scalability and responsiveness, especially when robots have partial information, unreliable communication, or changing tasks.

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114 papers16 papers in the last four weeks, down 16% on the four weeks before. 0.1% of all new papers.

DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models
Jun 12, 2026Wenhao Yu, Yu'ang Xie, Yifan Duan +5Multi-Agent LLM Systems Multi-Robot Coordination
University of Science and Technology of China (USTC) · University of Macau (UM)
Optimality-Preserving Decomposition for Scalable QAOA in Natural-Language-Guided Multi-Drone Assignment
Jun 12, 2026Junyeop Bang, Byongho Lee, Dohyun An +1Multi-Robot Task Allocation Quantum Approximate Optimization Algorithm
Department of Smart Convergence Korea University Republic of Korea · School of Electrical Engineering Korea University Republic of Korea
Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
Jun 7, 2026Michal P. Podolinsky, Neel P. Bhatt, Pranay Samineni +3Active Perception Image Segmentation
The University of Texas at Austin
Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems
Jun 4, 2026Cheng Ren, Ming Li, Xinping Guan +1Multi-Agent Coordination Robotics
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China
Market-Based Replanning for Safety-Critical UAV Swarms in Search and Rescue Missions
Jun 1, 2026Luiz Giacomossi, Andrea Haglund, Claire Namatovu +6Aerial Robotics Swarm Robotics
Mälardalen University (MDU) Västerås, Sweden
Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems
May 30, 2026Thien Tran, Jonathan Kua, Thuong Hoang +3Multi-Agent Orchestration Multi-Robot Systems
Deakin University, Australia · RMIT University, Vietnam · Zhejiang University, China +1
From Task Allocation to Risk Clearing: A Unifying Interface for Mixed Human-Agent Societies
May 26, 2026Vassilis VassiliadesMulti-Agent Coordination Multi-Robot Task Allocation
CYENS - Centre of Excellence, Nicosia, Cyprus
Heterogeneous AAV Logistics Task Allocation: A Reinforcement Learning Enhanced Overlapping Coalition Formation Game Approach
May 26, 2026Yuze Zhou, Jingliang Sun, Junzhi Li +3Reinforcement Learning Transformer-Based RL
Beijing Institute of Technology, Beijing 100081, China · Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing 100081, China · Beijing Institute of Technology Chongqing Innovation Center, Chongqing, 401121, China +1
Multi-Robot Box Transport over Different Surfaces with Decentralized Role-based Proportional Control
May 26, 2026Aditya Bhatt, Himavarshini Yarragangu, Urvish Shah +2Robotic Control Multi-Robot Motion Planning
Mechanical & Aerospace Eng., University at Buffalo, Buffalo, NY
Acting on the Unseen: Communication-Free Collaborative Filtering for Decentralized Multi-Robot Task Allocation
May 25, 2026Alexander Apartsin, Yigal Meshulam, Yehudit ApersteinRobust Matrix Completion Multi-Robot Task Allocation
Holon Institute of Technology, Holon, Israel. · Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
Auction-Consensus Algorithm with Learned Bidding Scheme for Multi-Robot Systems
May 21, 2026Jose Rodriguez, Constantine Tarawneh, Sven Koenig +2Multi-Robot Coordination Multi-Robot Task Allocation
Department of Electrical and Computer Engineering, The University of Texas at Rio Grande Valley (UTRGV), Edinburg, USA · Department of Mechanical Engineering, UTRGV, Edinburg, TX, USA · Department of Computer Science, Donald Bren School of Information and Computer Sciences, University of California, Irvine +1
Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
May 20, 2026Riwa Karam, Ruoyu Lin, Brooks A. Butler +1Multi-Agent Collaboration Multi-Robot Coordination
Samueli School of Engineering, University of California, Irvine, Irvine, CA, 92697, USA. · University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
Graph Neural Planning and Predictive Control for Multi-Robot Communication-Constrained Unlabeled Motion Planning
May 19, 2026Manohari Goarin, Yang Zhou, Giuseppe LoiannoMulti-Robot Motion Planning Nonlinear MPC
New York University, NY 10012, USA · University of California Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA 94720, USA
Multi-Agent Cooperative Transportation: Optimal and Efficient Task Allocation and Path Finding
May 15, 2026Ning Zhou, Nikolai W. F. Bode, Edmund R. HuntMulti-Agent System Optimization Multi-Robot Coordination
University of Bristol
Optimizing Line Segment Inspection with Limited-Range Drones
May 15, 2026José-Miguel Díaz-Báñez, José-Manuel Higes, Alina Kasiuk +1Aerial Robotics Combinatorial Optimization
Department of Applied Mathematics, University of Seville, Spain
Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
May 9, 2026Jiaxin Liu, Peng Yang, Yuping Li +1Combinatorial Optimization Robotics
Institution of Data and Information, Shenzhen International Graduate School, Tsinghua University, Nanshan District, Shenzhen 518055, China
HULK: Large-scale Hierarchical Coordination under Continual and Uncertain Temporal Tasks
May 9, 2026Qingyuan Luo, Jie Li, Meng GuoMulti-Agent Coordination Hierarchical Planning
the Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100871, China · National University of Defense Technology, Hunan 410073, China
Melding LLM and temporal logic for reliable human-swarm collaboration in complex scenarios
May 8, 2026Junfeng Chen, Yuxiao Zhu, An Zhuo +6Swarm Robotics Large Language Model-Based Robot Planning
School of Advanced Manufacturing and Robotics, Peking University, Beĳing & 100871, China. · 2Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou & 215316, China. · School of Automaton, Southeast University, Nanjing & 210096, China. +1
Many-to-Many Multi-Agent Pickup and Delivery
May 8, 2026Ethan Schneider, Jingkai Chen, Tianyi Gu +3Multi-Agent System Optimization Multi-Robot Task Allocation
Institute of Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA · Symbotic Inc., Wilmington, MA 01887, USA · Northeastern University, Boston, MA 02115, USA
Alternating Target-Path Planning for Scalable Multi-Agent Coordination
May 8, 2026Yu Kumagai, Keisuke OkumuraMulti-Agent Coordination Multi-Robot Task Allocation
1Hitotsubashi University, Japan · 2National Institute of Advanced Industrial Science and Technology (AIST), Japan
Coordination Matters: Process-Aware Evaluation of Cooperative Multi-Agent Reinforcement Learning
May 7, 2026Maria Ana Cardei, Matthew Landers, Afsaneh DoryabMulti-Agent Coordination AI Agent Benchmarks
University of Virginia
ARMATA: Auto-Regressive Multi-Agent Task Assignment
May 5, 2026Yazan Youssef, Aboelmagd Noureldin, Sidney GivigiMulti-Agent System Optimization Multi-Robot Task Allocation
Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada · Department of Electrical and Computer Engineering at the Royal Military College of Canada, and the Department of Electrical and Computer Engineering at Queen’s University, both in Kingston, Canada · School of Computing, Queen’s University, Kingston, ON, Canada
SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems
May 5, 2026Yibang Tang, Yifan Yang, Jingyuan Wang +2Mobile Robotics Robotic RL
School of Computer Science and Engineering, Beihang University, Beijing, China · Geekplus Technology Co., Ltd., Beijing, China
AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs
May 4, 2026John TadrousMulti-Robot Systems Age of Information
Electrical and Computer Engineering Department, Gonzaga University, WA 99258
Framework for Collaborative Operation of Autonomous Delivery Vehicles Within a Marshaling Yard
Apr 30, 2026James O'Hara, Karl Wunderlich, Gregory StevensMulti-Agent Orchestration Autonomous Driving
Autonomous Systems Research Center · Noblis Inc · M-City +1
Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments
Apr 26, 2026Yuxiang Li, Kun Chen, Jiancheng Wang +3Multi-Robot Motion Planning Multi-Robot Systems
College of Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hongkong 999077, China
DM $^3$ -Nav: Decentralized Multi-Agent Multimodal Multi-Object Semantic Navigation
Apr 23, 2026Amin Kashiri, Atharva Jamsandekar, Yasin YazıcıoğluVision-Language Navigation Multi-Robot Coordination
Northeastern University, Boston, USA · Department of Electrical and Computer Engineering at Northeastern University, Boston, MA
Logic-Based Verification of Task Allocation for LLM-Enabled Multi-Agent Manufacturing Systems
Apr 18, 2026Jonghan Lim, Mostafa Tavakkoli Anbarani, Rômulo Meira-Góes +1Multi-Agent Control Formal Verification
Department of Industrial and Manufacturing Engineering, Pennsylvania State University, State College, USA · Department of Mechanical Engineering, Pennsylvania State University, State College, USA · Department of Electrical Engineering, Pennsylvania State University, State College, USA
DeCoNav: Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation
Apr 14, 2026Sunyao Zhou, Yunzi Wu, Tianhang Wang +5Multi-Agent Coordination Vision-Language Navigation
Institute of Artificial Intelligence (TeleAI), China Telecom · Fudan University · Tongji University
Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach
Apr 13, 2026Maria G. Mendoza, Pan-Yang Su, Bryce L. Ferguson +1Multi-Robot Task Allocation Multi-Agent Resource Allocation
Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States · Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States · Thayer School of Engineering, Dartmouth College, Hanover, NH, United States

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DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models

Wenhao Yu, Yu'ang Xie, Yifan Duan, Jie Peng+4

University of Science and Technology of China (USTC) · University of Macau (UM)

Multi-Agent LLM Systems Multi-Robot Coordination Multi-Robot Task Allocation

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Large language models (LLMs) provide robots with richer task understanding and adaptability, making them promising for coordinating heterogeneous multi-robot systems in long-horizon tasks. Despite this potential, several challenges remain underexplored: (1) Centralized LLM schedulers scale poorly as team size and environmental complexity increase. A single model must process excessive contextual information, and long-context approximation may degrade reasoning quality; (2) Existing task formulations insufficiently consider dynamic settings, while robust adaptation to evolving task conditions is essential for real-world deployment; (3) Domain-specific data scarcity limits specialized robotic reasoning, making proprietary general-purpose models inefficient for expert tasks. To address these limitations, we propose DynaHMRC, a decentralized framework in which each robot acts as a role-aware LLM agent. This design mitigates the single-model context bottleneck and supports flexible collaboration across heterogeneous team configurations. DynaHMRC organizes collaboration as a four-stage closed-loop process: self-description, task allocation with leadership bidding, leader election, and reflective execution, supported by executable robot interfaces. We further develop a benchmark covering three task families, four dynamic variations, and six team configurations to systematically study dynamic task modeling. In addition, we conduct an empirical analysis to guide the construction of domain-specific expert datasets and fine-tune pretrained LLMs to improve specialized competence. Experiments show that DynaHMRC achieves higher success rates than strong baselines with fewer action and communication steps, while demonstrating promising scalability trends as team size grows within the evaluated settings.