  This paper proposes a novel method for randomized bin-picking based on
learning. When a two-fingered gripper tries to pick an object from the pile, a
finger often contacts a neighboring object. Even if a finger contacts a
neighboring object, the target object will be successfully picked depending on
the configuration of neighboring objects. In our proposed method, we use the
visual information on neighboring objects to train the discriminator.
Corresponding to a grasping posture of an object, the discriminator predicts
whether or not the pick will be successful even if a finger contacts a
neighboring object. We examine two learning algorithms, the linear support
vector machine (SVM) and the random forest (RF) approaches. By using both
methods, we demonstrate that the picking success rate is significantly higher
than with conventional methods without learning.
