基于学习的方法是否已准备好用于真实世界室内导航？一个关于模仿学习的案例研究
Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learning
AI总结：
本文研究模仿学习在真实室内导航中的可行性，利用专家演示训练基于RGB、LiDAR及多模态的导航策略，并与传统势场法在实体机器人上对比，证明IL具有实用价值但动态环境表现受限。
摘要：
传统的室内机器人导航方法在适应受限场景时能够提供可靠的解决方案，但在部署到更复杂的环境中时缺乏灵活性，或需要人工重新调参。相比之下，基于学习的方法直接从传感器数据和环境交互中学习，从而更容易适应新环境。尽管在学习导航策略方面已有大量工作被提出，但基于学习的方法很少与传统导航方法进行直接比较，这一问题阻碍了它们在通用导航场景中被最终接受。在本工作中，我们探索了模仿学习（imitation learning, IL）用于室内导航的可行性，利用专家（操纵杆）演示来训练基于RGB图像、LiDAR以及二者组合的多种导航策略网络，并将我们的IL方法与传统基于势场的导航方法进行比较。我们在一个配备2D LiDAR和摄像头的物理移动机器人平台上，于室内大学环境中对该方法进行了评估。我们的多模态模型在大多数场景中展现出优越的导航能力，但在动态环境中面临挑战，这可能是由于演示数据的多样性有限所致。尽管如此，直接从数据中学习并跨布局泛化的能力表明，IL可以成为一种实用的导航方法，并且有潜力作为后续终身学习的有用初始化策略。
英文摘要：
Traditional indoor robot navigation methods provide a reliable solution when adapted to constrained scenarios, but lack flexibility or require manual re-tuning when deployed in more complex settings. In contrast, learning-based approaches learn directly from sensor data and environmental interactions, enabling easier adaptability. While significant work has been presented in the context of learning navigation policies, learning-based methods are rarely compared to traditional navigation methods directly, which is a problem for their ultimate acceptance in general navigation contexts. In this work, we explore the viability of imitation learning (IL) for indoor navigation, using expert (joystick) demonstrations to train various navigation policy networks based on RGB images, LiDAR, and a combination of both, and we compare our IL approach to a traditional potential field-based navigation method. We evaluate the approach on a physical mobile robot platform equipped with a 2D LiDAR and a camera in an indoor university environment. Our multimodal model demonstrates superior navigation capabilities in most scenarios, but faces challenges in dynamic environments, likely due to limited diversity in the demonstrations. Nevertheless, the ability to learn directly from data and generalise across layouts suggests that IL can be a practical navigation approach, and potentially a useful initialisation strategy for subsequent lifelong learning.
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。
