  Owing to human labor shortages, the automation of labor-intensive manual
waste-sorting is needed. The goal of automating the waste-sorting is to replace
the human role of robust detection and agile manipulation of the waste items by
robots. To achieve this, we propose three methods. First, we propose a combined
manipulation method using graspless push-and-drop and pick-and-release
manipulation. Second, we propose a robotic system that can automatically
collect object images to quickly train a deep neural network model. Third, we
propose the method to mitigate the differences in the appearance of target
objects from two scenes: one for the dataset collection and the other for waste
sorting in a recycling factory. If differences exist, the performance of a
trained waste detector could be decreased. We address differences in
illumination and background by applying object scaling, histogram matching with
histogram equalization, and background synthesis to the source target-object
images. Via experiments in an indoor experimental workplace for waste-sorting,
we confirmed the proposed methods enable quickly collecting the training image
sets for three classes of waste items, i.e., aluminum can, glass bottle, and
plastic bottle and detecting them with higher performance than the methods that
do not consider the differences. We also confirmed that the proposed method
enables the robot quickly manipulate them.
