在人机交互中利用人类视线与增强现实教授未知物体
Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interaction
AI总结：
本研究提出结合眼动追踪与增强现实的多模态人机交互系统，让人类通过视线指导机器人学习未知物体，使机器人摆脱对预训练数据集的依赖，检测性能达到当前最优水平且不受预设类别限制。
摘要：
机器人凭借其出色的工作能力、精度、效率和可扩展性，正广泛应用于各类环境中。人工智能（尤其是机器学习）的发展进一步推动了这一趋势。通过采用先进的神经网络，机器人具备了检测周边物体并与之交互的能力。然而，一个显著的缺陷在于，这些物体检测模型高度依赖海量数据集和大量训练数据。当机器人的具体部署位置及周边环境无法提前获知时，这一问题会变得尤为棘手。物体种类繁多且不断增加，仅靠现有数据集几乎不可能覆盖所有现存物体。本论文的目标是在人机交互（Human-Robot Interaction, HRI）场景下教会机器人识别未知物体，使其摆脱对数据的依赖，不再受限于预设场景。在此背景下，眼动追踪与增强现实的结合形成了强大的协同效应，让人类教师能够与机器人沟通，并通过人类视线轻松指出目标物体。这种整体性方法促成了一个多模态HRI系统的开发，该系统使机器人能够在3D空间中识别并视觉分割感兴趣物体（Objects of Interest）。借助人类提供的类别信息，机器人能够学习这些物体，并在之后重新检测到它们。得益于这种基于HRI的教学所获取的知识，机器人的物体检测能力展现出与在海量数据集上训练的SOTA（state-of-the-art，当前最优）物体检测器相当的性能，且不受预设类别的限制，彰显了其通用性与适应性。
英文摘要：
Robots are becoming increasingly popular in a wide range of environments due to their exceptional work capacity, precision, efficiency, and scalability. This development has been further encouraged by advances in Artificial Intelligence, particularly Machine Learning. By employing sophisticated neural networks, robots are given the ability to detect and interact with objects in their vicinity. However, a significant drawback arises from the underlying dependency on extensive datasets and the availability of substantial amounts of training data for these object detection models. This issue becomes particularly problematic when the specific deployment location of the robot and the surroundings, are not known in advance. The vast and ever-expanding array of objects makes it virtually impossible to comprehensively cover the entire spectrum of existing objects using preexisting datasets alone. The goal of this dissertation was to teach a robot unknown objects in the context of Human-Robot Interaction (HRI) in order to liberate it from its data dependency, unleashing it from predefined scenarios. In this context, the combination of eye tracking and Augmented Reality created a powerful synergy that empowered the human teacher to communicate with the robot and effortlessly point out objects by means of human gaze. This holistic approach led to the development of a multimodal HRI system that enabled the robot to identify and visually segment the Objects of Interest in 3D space. Through the class information provided by the human, the robot was able to learn the objects and redetect them at a later stage. Due to the knowledge gained from this HRI based teaching, the robot's object detection capabilities exhibited comparable performance to state-of-the-art object detectors trained on extensive datasets, without being restricted to predefined classes, showcasing its versatility and adaptability.
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。
