Autonomous dishwasher loading from cluttered trays using pre‐trained deep neural networks

Abstract

Abstract 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 article presents a novel learning‐based solution without a training phase using 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 are shared in a public repository.