从专家数据学习无人船自主靠泊，生成类人操作轨迹。
Learning Autonomous Docking Operation of Fully Actuated Autonomous Surface Vessel from Expert data
- 用逆强化学习从专家轨迹中学习奖励函数
- 仿真验证在不同环境下生成类人靠泊行为
- 结合环境感知与运动状态提升控制精度
本文提出一种基于专家示范数据的全驱动无人水面艇自主靠泊方法。将靠泊问题建模为模仿学习任务，采用逆强化学习（IRL）从专家轨迹中学习奖励函数。设计两阶段神经网络架构，融合传感器环境信息与船舶运动学状态至奖励函数。利用学习到的奖励函数与运动规划器生成靠泊轨迹。仿真实验表明，该方法能在多种环境配置下生成类人化靠泊行为，有效实现自主靠泊。
原文摘要 · Abstract (English)
This paper presents an approach for autonomous docking of a fully actuated autonomous surface vessel using expert demonstration data. We frame the docking problem as an imitation learning task and employ inverse reinforcement learning (IRL) to learn a reward function from expert trajectories. A two-stage neural network architecture is implemented to incorporate both environmental context from sensors and vehicle kinematics into the reward function. The learned reward is then used with a motion planner to generate docking trajectories. Experiments in simulation demonstrate the effectiveness of this approach in producing human-like docking behaviors across different environmental configurations.
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