mlsanityData to 14 Oct 2026

Distributed Multi Robot Lunar Cargo Transportation via Phase Decomposed Reinforcement Learning

arXiv 2607.00160 ↗First posted 30 Jun 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Modular reconfigurable robotic systems provide a scalable solution for cooperative surface operations in future lunar missions. However, cooperative cargo transportation remains challenging due to morphology-dependent topology changes, strong payload-induced coupling, long-horizon decision making, and safety constraints. This paper proposes a phase-decomposed reinforcement learning framework for cooperative cargo transport with distributed robotic units. The task is decomposed into lifting, transportation, and placement, each optimized with a dedicated joint-state policy capturing inter-agent coupling. Centralized training promotes stable convergence, while deployment uses onboard proprioception for control and OptiTrack motion capture for ground-truth evaluation and post-processed metrics. A deterministic phase controller expressed in Markov state representation regulates transitions between stages, and a failure-sensitive synchronization mechanism ensures coordinated progression and safety-aware halting during real-world execution. The framework is evaluated in simulation and through controlled field experiments at a JAXA space exploration test facility. Results demonstrate reliable cooperative transport across all stages in both simulation and hardware experiments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 23 references that are in the map. First posted on arXiv 30 Jun 2026.

Builds on

11 of 23 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots23 Oct

Other references · 10

  1. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  2. 2021Learning Modular Robot Control Policiescited by 22
  3. 2022Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewardscited by 16
  4. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 16
  5. 2019Distributed Learning of Decentralized Control Policies for Articulated Mobile Robotscited by 7
  6. 2024Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigationcited by 5
  7. 2023Decentralized Motor Skill Learning for Complex Robotic Systemscited by 4
  8. 2022On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarioscited by 3
  9. 2025Robust and Modular Multi-Limb Synchronization in Motion Stack for Space Robots with Trajectory Clamping via Hyperspherecited by 3
  10. 2025MoonBot: Modular and On-Demand Reconfigurable Robot Toward Moon Base Constructioncited by 2

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  1. 2025Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks
  2. 2026Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
  3. 2025Onboard Mission Replanning for Adaptive Cooperative Multi-Robot Systems
  4. 2025Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning
  5. 2025Space Robotics Bench: Robot Learning Beyond Earth
  6. 2024Learning Decentralized Multi-Biped Control for Payload Transport
  7. 2024Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
  8. 2026Learning Safety-Constrained Whole-Body Loco-Manipulation for Lunar Surface Operations ↗