所见即所抓：基于近眼追踪的用户友好抓取
What You See is What You Grasp: User-Friendly Grasping Guided by Near-eye-tracking
AI总结：
提出一种结合近眼追踪与Transformer抓取模型的视觉引导机器人界面，仅通过视线实现用户意图推断与抓取操作，在多个数据集上验证了低误差与良好性能，为辅助机器人提供概念验证。
摘要：
这项工作提出了一种下一代人机交互界面，仅通过视觉即可推断并实现用户的操作意图。具体而言，我们开发了一个集成近眼追踪与机器人操作的系统，以支持用户指定的动作（如抓取、拾取-放置等），其中视觉信息与人类注意力相结合，为期望的机器人动作建立映射。为实现视觉引导的操作，我们开发了一种头戴式近眼追踪设备，实时追踪眼球运动，从而识别用户的视觉注意力。为提高抓取性能，我们进一步开发了基于Transformer的抓取模型。采用堆叠的Transformer块提取分层特征，在每个阶段扩展通道数，同时压缩特征图的分辨率。实验验证表明，眼动追踪系统具有较低的注视估计误差，抓取系统在多个抓取数据集上取得了有前景的结果。这项工作为基于注视交互的辅助机器人提供了概念验证，有望在老年人及上肢残疾者的日常生活中发挥重要作用。演示视频可在 https://www.youtube.com/watch?v=yuZ1hukYUrM 观看。
英文摘要：
This work presents a next-generation human-robot interface that can infer and realize the user's manipulation intention via sight only. Specifically, we develop a system that integrates near-eye-tracking and robotic manipulation to enable user-specified actions (e.g., grasp, pick-and-place, etc), where visual information is merged with human attention to create a mapping for desired robot actions. To enable sight guided manipulation, a head-mounted near-eye-tracking device is developed to track the eyeball movements in real-time, so that the user's visual attention can be identified. To improve the grasping performance, a transformer based grasp model is then developed. Stacked transformer blocks are used to extract hierarchical features where the volumes of channels are expanded at each stage while squeezing the resolution of feature maps. Experimental validation demonstrates that the eye-tracking system yields low gaze estimation error and the grasping system yields promising results on multiple grasping datasets. This work is a proof of concept for gaze interaction-based assistive robot, which holds great promise to help the elder or upper limb disabilities in their daily lives. A demo video is available at https://www.youtube.com/watch?v=yuZ1hukYUrM
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。
