  Self-supervised learning methods are attractive candidates for automatic
object picking. However, the trial samples lack the complete ground truth
because the observable parts of the agent are limited. That is, the information
contained in the trial samples is often insufficient to learn the specific
grasping position of each object. Consequently, the training falls into a local
solution, and the grasp positions learned by the robot are independent of the
state of the object. In this study, the optimal grasping position of an
individual object is determined from the grasping score, defined as the
distance in the feature space obtained using metric learning. The closeness of
the solution to the pre-designed optimal grasping position was evaluated in
trials. The proposed method incorporates two types of feedback control: one
feedback enlarges the grasping score when the grasping position approaches the
optimum; the other reduces the negative feedback of the potential grasping
positions among the grasping candidates. The proposed online self-supervised
learning method employs two deep neural networks. : SSD that detects the
grasping position of an object, and Siamese networks (SNs) that evaluate the
trial sample using the similarity of two input data in the feature space. Our
method embeds the relation of each grasping position as feature vectors by
training the trial samples and a few pre-samples indicating the optimum
grasping position. By incorporating the grasping score based on the feature
space of SNs into the SSD training process, the method preferentially trains
the optimum grasping position. In the experiment, the proposed method achieved
a higher success rate than the baseline method using simple teaching signals.
And the grasping scores in the feature space of the SNs accurately represented
the grasping positions of the objects.
