Autonomous dishwasher loading is a benchmark problem in robotics that
highlights the challenges of robotic perception, planning and
manipulation in an unstructured environment. Current approaches resort
to a specialized solution, however, these technologies are not viable in
a domestic setting. Learning-based solutions seem promising for a
general purpose solutions, however, they require large amounts of
catered data, to be applied in real-world scenarios. This paper presents
a novel solution based on pre-trained object detection networks. By
developing a perception, planning and manipulation framework around an
off-the-shelf object detection network, we are able to develop robust
pick-and-place solutions that are easy to develop and general purpose
requiring only a RGB feedback and a pinch gripper. Analysis of a
real-world canteen tray data is first performed and used for developing
our in-lab experimental setup. Our results obtained from real-world
scenarios indicate that such approaches are highly desirable for
plug-and-play domestic applications with limited calibration. All the
associated data and code of this work is shared in a public repository.