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

Initial Experiments on Learning-Based Randomized Bin-Picking Allowing Finger Contact with Neighboring Objects