Abstract
Opening doors and drawers will be an important ability for future service robots used in domestic and industrial environments. However, in recent years, the methods for opening doors and drawers have become more diverse and difficult for robots to determine and manipulate. We can divide doors into three distinct handling types: regular handles, hidden handles, and push mechanisms. While extensive research has been done on the detection and handling of regular handles, the other types of handling have not been explored as much. In this paper, we set out to classify the types of cabinet door handling types. To this end, we collect and label a dataset consisting of RGB-D images of cabinets in their natural environment. As part of the dataset, we provide images of humans demonstrating the handling of these doors. We detect the poses of human hands and then train a classifier to determine the type of cabinet door handling. With this research, we hope to provide a starting point for exploring the different types of cabinet door openings in real-world environments.