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Current robotic hand manipulation narrowly operates with objects inpredictable positions in limited environments. Thus, when the location of thetarget object deviates severely from the expected location, a robot sometimesresponds in an unexpected way, especially when it operates with a human. Forsafe robot operation, we propose the EXit-aware Object Tracker (EXOT) on arobot hand camera that recognizes an object's absence during manipulation. Therobot decides whether to proceed by examining the tracker's bounding box outputcontaining the target object. We adopt an out-of-distribution classifier formore accurate object recognition since trackers can mistrack a background as atarget object. To the best of our knowledge, our method is the first approachof applying an out-of-distribution classification technique to a trackeroutput. 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 UR5erobot. Then we test our tracker on the UR5e robot in real-time with aconveyor-belt sushi task, to examine the tracker's ability to track targetdishes and to determine the exit status. Our tracker shows 38% higherexit-aware performance than a baseline method. The dataset and the code will bereleased at https://github.com/hskAlena/EXOT.

EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object