  Owing to human labor shortages, the automation of labor-intensive manual
waste-sorting is needed. The goal of automating waste-sorting is to replace the
human role of robust detection and agile manipulation of waste items with
robots. To achieve this, we propose three methods. First, we provide a combined
manipulation method using graspless push-and-drop and pick-and-release
manipulation. Second, we provide a robotic system that can automatically
collect object images to quickly train a deep neural-network model. Third, we
provide a method to mitigate the differences in the appearance of target
objects from two scenes: one for dataset collection and the other for waste
sorting in a recycling factory. If differences exist, the performance of a
trained waste detector may decrease. 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 confirm
that the proposed methods enable quick collection of the training image sets
for three classes of waste items (i.e., aluminum can, glass bottle, and plastic
bottle) and detection with higher performance than the methods that do not
consider the differences. We also confirm that the proposed method enables the
robot quickly manipulate the objects.
