SMaRT-Tug: 物理ベースのタグボート・バージ協調操作のための構造化マルチエージェント強化学習
SMaRT-Tug: Structured Multi-Agent Reinforcement Learning for Physics-Based Tugboat-Barge Collaborative Manipulation
複数のタグボートがバージを協調して押す操作を、物理ベースのGPUシミュレータと構造化制御事前知識を組み合わせた分散型MAPPOで学習し、PIDや集中型PPOより高い性能とゼロショット一般化を実証した論文。
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著者: Junkai Lu, Jiadong Zhao, Jiacheng Zhang, Wenqi Zhao, Hao Gen Chia, Qun Shen Png, Germaine Ee, Chengyang He, Yifeng Zhang, Nathanael Tan, Guillaume Sartoretti
分類: cs.RO
原文アブストラクト
Autonomous tugboating is central for automating maritime operations such as port logistics and vessel maneuvering, where multiple tugboats must cooperatively transport/manipulate a larger vessel. Collaborative pushing in this setting is challenging due to coupled hydrodynamics, low resistance, strong environmental disturbances, underactuated barge dynamics, and contact-rich interactions. Conventional control methods often rely on simplified models and fixed configurations, which limit their adaptability, while learning-based approaches are constrained by the lack of scalable and physically realistic training environments. We address these challenges by introducing a physics-based, GPU-accelerated simulation and learning framework for collaborative tugboat manipulation. Our simulator incorporates a customized buoyancy model, wave modeling, and hydrodynamic resistance, and supports large-scale multi-agent training under marine dynamics. In this simulator, we train a decentralized MAPPO (Multi-Agent PPO) policy augmented with a structured control prior (SCP) to improve training stability and maintain feasible pushing configurations. We evaluate our learned policy on straight-line transit, turning, and deceleration tasks, where we show that our decentralized framework yields more reliable and accurate maneuvering performance compared to a PID-based controller and a centralized PPO baseline. We further demonstrate zero-shot generalization to more challenging sea states and advanced maneuvers, as well as zero-shot scalability to larger teams of three and four tugboats despite training with only two agents.
