Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a robot hand camera that recognizes an object's absence during manipulation. The robot decides whether to proceed by examining the tracker's bounding box output containing the target object. We adopt an out-of-distribution classifier for more accurate object recognition since trackers can mistrack a background as a target object. To the best of our knowledge, our method is the first approach of applying an out-of-distribution classification technique to a tracker output. We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot. Then we test our tracker on the UR5e robot in real-time with a conveyor-belt sushi task, to examine the tracker's ability to track target dishes and to determine the exit status. Our tracker shows 38% higher exit-aware performance than a baseline method. The dataset and the code will be released at https://github.com/hskAlena/EXOT.

翻译：摘要：当前机械手操作在有限环境中仅能处理位置可预测的物体。因此，当目标物体位置与预期位置出现严重偏差时，机器人有时会做出意外响应，尤其是在与人协作的场景中。为实现安全机器人操作，我们提出在机械手相机上部署的出口感知目标跟踪器（EXOT），该跟踪器可在操作过程中识别目标物体的缺失。机器人通过检查包含目标物体的跟踪器边界框输出来决定是否继续执行操作。针对跟踪器可能将背景误判为目标物体的问题，我们采用分布外分类器以提高物体识别精度。据我们所知，本方法是首个将分布外分类技术应用于跟踪器输出的研究工作。我们在第一人称视频基准数据集TREK-150以及自主采集的UR5e机器人数据集RMOT-223上评估了该方法的性能。进一步地，我们在传送带寿司任务场景中，基于UR5e机器人实时测试了跟踪器追踪目标餐盘及判断出口状态的能力。实验结果表明，本跟踪器的出口感知性能较基线方法提升38%。相关数据集与代码将发布于https://github.com/hskAlena/EXOT。