mlsanityData to 14 Oct 2026

Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport

arXiv 2404.02362 ↗First posted 2 Apr 2024cs.ROcited by 1

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

Abstract (arXiv)

Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various weights are mixed and the number of robots and objects varies. This paper presents Task-priority Intermediated Hierarchical Distributed Policies (TIHDP), a multi-agent Reinforcement Learning (RL) framework that addresses these challenges through a hierarchical policy structure. TIHDP consists of three layers: task allocation policy (higher layer), dynamic task priority (intermediate layer), and robot control policy (lower layer). Whereas the dynamic task priority layer can manipulate the priority of any object to be transported by receiving global object information and communicating with other robots, the task allocation and robot control policies are restricted by local observations/actions so that they are not affected by changes in the number of objects and robots. Through simulations and real-robot demonstrations, TIHDP shows promising adaptability and performance of the learned multi-robot cooperative transport, even in environments with varying numbers of robots and objects. Video is available at https://youtu.be/Rmhv5ovj0xM

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 10 of its 19 references that are in the map. First posted on arXiv 2 Apr 2024.

Builds on

10 of 19 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  4. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  5. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  6. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
  7. 2022DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Realitycited by 149
  8. 2020Scalable Reinforcement Learning Policies for Multi-Agent Controlcited by 12
  9. 2021Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transportcited by 5
  10. 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. 2026Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specificationscited by 0

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  1. 2024Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks
  2. 2024Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty
  3. 2024Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning
  4. 2026Hierarchical Multi-agent Reinforcement Learning for Warehouse Robot Coordination under Communication Loss ↗
  5. 2026DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport
  6. 2025Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems
  7. 2024Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
  8. 2026Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning