  This paper shows experimental results on learning based randomized
bin-picking combined with iterative visual recognition. We use the random
forest to predict whether or not a robot will successfully pick an object for
given depth images of the pile taking the collision between a finger and a
neighboring object into account. For the discriminator to be accurate, we
consider estimating objects' poses by merging multiple depth images of the pile
captured from different points of view by using a depth sensor attached at the
wrist. We show that, even if a robot is predicted to fail in picking an object
with a single depth image due to its large occluded area, it is finally
predicted as success after merging multiple depth images. In addition, we show
that the random forest can be trained with the small number of training data.
