{"ID":5552822,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T20:14:26.82372516Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00160","arxiv_id":"2607.00160","title":"Distributed Multi Robot Lunar Cargo Transportation via Phase Decomposed Reinforcement Learning","abstract":"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.","short_abstract":"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 constraint...","url_abs":"https://arxiv.org/abs/2607.00160","url_pdf":"https://arxiv.org/pdf/2607.00160v1","authors":"[\"Ashutosh Mishra\",\"Elian Neppel\",\"Shreya Santra\",\"Antoine Jonquières\",\"Muhammad Athallah Naufal\",\"Kentaro Uno\",\"Kazuya Yoshida\"]","published":"2026-06-30T20:36:03Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false}