  Autonomous sorting is a crucial task in industrial robotics which can be very
challenging depending on the expected amount of automation. Usually, to decide
where to sort an object, the system needs to solve either an instance retrieval
(known object) or a supervised classification (predefined set of classes)
problem. In this paper, we introduce a new decision making module, where the
robotic system chooses how to sort the objects in an unsupervised way. We call
this problem Unsupervised Robotic Sorting (URS) and propose an implementation
on an industrial robotic system, using deep CNN feature extraction and standard
clustering algorithms. We carry out extensive experiments on various standard
datasets to demonstrate the efficiency of the proposed image clustering
pipeline. To evaluate the robustness of our URS implementation, we also
introduce a complex real world dataset containing images of objects under
various background and lighting conditions. This dataset is used to fine tune
the design choices (CNN and clustering algorithm) for URS. Finally, we propose
a method combining our pipeline with ensemble clustering to use multiple images
of each object. This redundancy of information about the objects is shown to
increase the clustering results.
