用大模型和强化学习实现人机协同的船舶船体巡检，降低操作负担。
Shared Autonomy through LLMs and Reinforcement Learning for Applications to Ship Hull Inspections
- 融合大模型与行为树，实现直观任务指令与可解释控制
- 在模拟与湖面环境中验证系统能减少认知负荷并提升意图对齐
- 适合安全关键型海洋机器人应用，尤其需人机协作的场景
共享自主是机器人系统中极具前景的范式，尤其在复杂、高风险且不确定的海事环境中，亟需高效的人机协作。本文探索三种互补方法以推进异构海洋机器人集群的共享自主：(i) 利用大语言模型（LLMs）实现直观的高层任务设定，支持船体巡检任务；(ii) 在多智能体设置中引入人在回路的交互框架，实现自适应、意图感知的协调；(iii) 开发基于行为树的模块化任务管理器，提供可解释且灵活的任务控制。仿真及类湖环境中的初步结果表明，该多层架构能有效降低操作员认知负荷、增强透明度，并提升行为与人类意图的一致性。当前工作聚焦于完整集成各组件、优化协调机制，并在真实港口场景中验证系统效能。本研究为安全关键型海事机器人应用建立了模块化、可扩展的信任型人机协同自主基础。
原文摘要 · Abstract (English)
Shared autonomy is a promising paradigm in robotic systems, particularly within the maritime domain, where complex, high-risk, and uncertain environments necessitate effective human-robot collaboration. This paper investigates the interaction of three complementary approaches to advance shared autonomy in heterogeneous marine robotic fleets: (i) the integration of Large Language Models (LLMs) to facilitate intuitive high-level task specification and support hull inspection missions, (ii) the implementation of human-in-the-loop interaction frameworks in multi-agent settings to enable adaptive and intent-aware coordination, and (iii) the development of a modular Mission Manager based on Behavior Trees to provide interpretable and flexible mission control. Preliminary results from simulation and real-world lake-like environments demonstrate the potential of this multi-layered architecture to reduce operator cognitive load, enhance transparency, and improve adaptive behaviour alignment with human intent. Ongoing work focuses on fully integrating these components, refining coordination mechanisms, and validating the system in operational port scenarios. This study contributes to establishing a modular and scalable foundation for trustworthy, human-collaborative autonomy in safety-critical maritime robotics applications.
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