从新手到熟练：基于强化学习的共享自主系统在多任务无人机任务中与飞行员通信
From Novice to Skilled: RL-based Shared Autonomy Communicating with Pilots in UAV Multi-Task Missions
AI总结：
本研究提出一种基于强化学习的共享自主系统，通过三个模块辅助无人机飞行员完成多任务，显著提升成功率，并验证了仿真训练向现实迁移的有效性。
摘要：
涉及检查和着陆任务的多任务无人机（UAV）任务对新手飞行员来说具有挑战性，这源于深度感知和控制界面的困难。我们提出了一种共享自主系统，并辅以补充信息显示，以帮助飞行员在无需任何培训的情况下成功完成多任务任务。我们的方法包含三个模块：（1）感知模块，将视觉信息编码为潜在表示；（2）策略模块，增强飞行员的动作；（3）信息增强模块，为飞行员提供额外信息。策略模块在仿真环境中使用模拟用户进行训练，并在用户研究（n=29）中无需修改地迁移到现实世界，同时采用了包括学习到的红/绿光反馈提示和增强现实显示在内的替代补充信息方案。飞行员意图对策略模块是未知的，并从飞行员的输入和无人机状态中推断得出。该助手将着陆和检查任务的成功率分别从[16.67%和54.29%]提高到[95.59%和96.22%]。有了该助手，经验不足的飞行员达到了与经验丰富的飞行员相似的性能。红/绿光反馈提示将检查任务所需时间减少了19.53%，轨迹长度减少了17.86%，参与者因其直观界面和提供安心感而将其评为首选条件。这项工作表明，简单的用户模型可以在仿真中训练共享自主系统，并迁移到物理任务中以估计用户意图，为飞行员提供有效的协助和信息。
英文摘要：
Multi-task missions for unmanned aerial vehicles (UAVs) involving inspection and landing tasks are challenging for novice pilots due to the difficulties associated with depth perception and the control interface. We propose a shared autonomy system, alongside supplementary information displays, to assist pilots to successfully complete multi-task missions without any pilot training. Our approach comprises of three modules: (1) a perception module that encodes visual information onto a latent representation, (2) a policy module that augments pilot's actions, and (3) an information augmentation module that provides additional information to the pilot. The policy module is trained in simulation with simulated users and transferred to the real world without modification in a user study (n=29), alongside alternative supplementary information schemes including learnt red/green light feedback cues and an augmented reality display. The pilot's intent is unknown to the policy module and is inferred from the pilot's input and UAV's states. The assistant increased task success rate for the landing and inspection tasks from [16.67% & 54.29%] respectively to [95.59% & 96.22%]. With the assistant, inexperienced pilots achieved similar performance to experienced pilots. Red/green light feedback cues reduced the required time by 19.53% and trajectory length by 17.86% for the inspection task, where participants rated it as their preferred condition due to the intuitive interface and providing reassurance. This work demonstrates that simple user models can train shared autonomy systems in simulation, and transfer to physical tasks to estimate user intent and provide effective assistance and information to the pilot.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
