  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.
