发表机构
Tohoku University; École Centrale de Lille; Institut Teknologi Bandung（东北大学; 里尔中央理工学院; 万隆理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出相位分解强化学习框架，将月球货物运输分解为提升、运输和放置三个阶段，分别优化联合状态策略，实现分布式机器人协同运输，仿真和硬件实验验证了可靠性。
AI 中文摘要
模块化可重构机器人系统为未来月球任务中的合作表面操作提供了可扩展的解决方案。然而，由于形态依赖的拓扑变化、强载荷引起的耦合、长时域决策以及安全约束，合作货物运输仍然具有挑战性。本文提出了一种用于分布式机器人单元合作货物运输的相位分解强化学习框架。任务被分解为提升、运输和放置三个阶段，每个阶段都通过捕获智能体间耦合的专用联合状态策略进行优化。集中式训练促进稳定收敛，而部署则使用机载本体感觉进行控制，并使用OptiTrack运动捕捉进行真实值评估和后处理度量。以马尔可夫状态表示表达的确定性相位控制器调节阶段之间的转换，而故障敏感同步机制确保协调推进和实际执行中的安全感知停止。该框架在仿真和JAXA空间探索测试设施的受控现场实验中进行了评估。结果表明，在仿真和硬件实验中，所有阶段均实现了可靠的合作运输。
英文摘要
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.
Comments8 pages, 9 Figures, Accepted at IROS2026
