Robotic manipulators are increasingly used to assist individuals with mobility impairments in object retrieval. However, the predominant joystick-based control interfaces can be challenging due to high precision requirements and unintuitive reference frames. Recent advances in human-robot interaction have explored alternative modalities, yet many solutions still rely on external screens or restrictive control schemes, limiting their intuitiveness and accessibility. To address these challenges, we present an egocentric, gaze-guided robotic manipulation interface that leverages a wearable Mixed Reality (MR) headset. Our system enables users to interact seamlessly with real-world objects using natural gaze fixation from a first-person perspective, while providing augmented visual cues to confirm intent and leveraging a pretrained vision model and robotic arm for intent recognition and object manipulation. Experimental results demonstrate that our approach significantly improves manipulation accuracy, reduces system latency, and achieves single-pass intention and object recognition accuracy greater than 88% across multiple real-world scenarios. These results demonstrate the system's effectiveness in enhancing intuitiveness and accessibility, underscoring its practical significance for assistive robotics applications.

翻译：机器人机械臂越来越多地用于协助行动不便者进行物体抓取。然而，当前主流的基于操纵杆的控制界面，由于对操作精度要求高且参考坐标系不够直观，往往给用户带来挑战。人机交互领域的最新进展探索了多种替代交互模态，但许多方案仍依赖于外部屏幕或限制性控制方案，从而限制了其直观性和可访问性。为应对这些挑战，我们提出了一种以自我为中心、由视线引导的机器人操控界面，该界面利用了可穿戴混合现实（MR）头显。我们的系统使用户能够从第一人称视角，通过自然的视线注视与现实世界中的物体进行无缝交互，同时提供增强视觉提示以确认操作意图，并利用预训练的视觉模型和机械臂进行意图识别与物体操控。实验结果表明，我们的方法显著提高了操控精度，降低了系统延迟，并在多种真实场景中实现了单次意图与物体识别准确率超过88%。这些结果证明了该系统在增强直观性和可访问性方面的有效性，凸显了其在辅助机器人应用中的实际意义。