  In this research, we tackle the problem of picking an object from randomly
stacked pile. Since complex physical phenomena of contact among objects and
fingers makes it difficult to perform the bin-picking with high success rate,
we consider introducing a learning based approach. For the purpose of
collecting enough number of training data within a reasonable period of time,
we introduce a physics simulator where approximation is used for collision
checking. In this paper, we first formulate the learning based robotic
bin-picking by using CNN (Convolutional Neural Network). We also obtain the
optimum grasping posture of parallel jaw gripper by using CNN. Finally, we show
that the effect of approximation introduced in collision checking is relaxed if
we use exact 3D model to generate the depth image of the pile as an input to
CNN.
