# Distributed Multi Robot Lunar Cargo Transportation via Phase Decomposed Reinforcement Learning

arXiv: https://arxiv.org/abs/2607.00160 · HTML: https://mlsanity.com/paper/2607.00160/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Learning Modular Robot Control Policies](https://mlsanity.com/paper/2105.10049/) (2021)
- [Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards](https://mlsanity.com/paper/2203.10229/) (2022)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots](https://mlsanity.com/paper/1901.08537/) (2019)
- [Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigation](https://mlsanity.com/paper/2403.14583/) (2024)
- [Decentralized Motor Skill Learning for Complex Robotic Systems](https://mlsanity.com/paper/2306.17411/) (2023)
- [On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios](https://mlsanity.com/paper/2207.09372/) (2022)
- [Robust and Modular Multi-Limb Synchronization in Motion Stack for Space Robots with Trajectory Clamping via Hypersphere](https://mlsanity.com/paper/2507.03934/) (2025)
- [MoonBot: Modular and On-Demand Reconfigurable Robot Toward Moon Base Construction](https://mlsanity.com/paper/2512.21853/) (2025)
- [Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots](https://mlsanity.com/paper/2510.20347/) (2025)

## 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.