  This paper proposes a iterative visual recognition system for learning based
randomized bin-picking. Since the configuration on randomly stacked objects
while executing the current picking trial is just partially different from the
configuration while executing the previous picking trial, we consider detecting
the poses of objects just by using a part of visual image taken at the current
picking trial where it is different from the visual image taken at the previous
picking trial. By using this method, we do not need to try to detect the poses
of all objects included in the pile at every picking trial. Assuming the 3D
vision sensor attached at the wrist of a manipulator, we first explain a method
to determine the pose of a 3D vision sensor maximizing the visibility of
randomly stacked objects. Then, we explain a method for detecting the poses of
randomly stacked objects. Effectiveness of our proposed approach is confirmed
by experiments using a dual-arm manipulator where a 3D vision sensor and the
two-fingered hand attached at the right and the left wrists, respectively.
