mlsanityData to 14 Oct 2026

Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks

arXiv 2404.11817 ↗First posted 18 Apr 2024cs.ROcited by 1

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

Multi-object transport using multi-robot systems has the potential for diverse practical applications such as delivery services owing to its efficient individual and scalable cooperative transport. However, allocating transportation tasks of objects with unknown weights remains challenging. Moreover, the presence of infeasible tasks (untransportable objects) can lead to robot stoppage (deadlock). This paper proposes a framework for dynamic task allocation that involves storing task experiences for each task in a scalable manner with respect to the number of robots. First, these experiences are broadcasted from the cloud server to the entire robot system. Subsequently, each robot learns the exclusion levels for each task based on those task experiences, enabling it to exclude infeasible tasks and reset its task priorities. Finally, individual transportation, cooperative transportation, and the temporary exclusion of tasks considered infeasible are achieved. The scalability and versatility of the proposed method were confirmed through numerical experiments with an increased number of robots and objects, including unlearned weight objects. The effectiveness of the temporary deadlock avoidance was also confirmed by introducing additional robots within an episode. The proposed method enables the implementation of task allocation strategies that are feasible for different numbers of robots and various transport tasks without prior consideration of feasibility.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 30 references that are in the map. First posted on arXiv 18 Apr 2024.

Builds on

7 of 30 references.

References · 7

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  3. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 155
  4. 2020Game-Theoretic Multiagent Reinforcement Learningcited by 94
  5. 2020Scalable Reinforcement Learning Policies for Multi-Agent Controlcited by 12
  6. 2021Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systemscited by 6
  7. 2022Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transportcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shiftscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
  2. 2026Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams
  3. 2024A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation
  4. 2026Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning
  5. 2026Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning
  6. 2024Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty
  7. 2026Shape Formation for the Cooperative Transportation of Arbitrary Objects Using Multi-Agent Reinforcement Learning
  8. 2026DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport