Exploiting trademark databases for robotic object fetching

Abstract

Service robots require the ability to recognize various household objects in order to carry out certain tasks, such as fetching an object for a person. Manually collecting information on all the objects a robot may encounter in a household is tedious and time-consuming; therefore this paper proposes the use of large-scale data from existing trademark databases. These databases contain logo images and a description of the goods and services the logo was registered under. For example, Pepsi is registered under soft drinks. We extend domain randomization in order to generate synthetic data to train a convolutional neural network logo detector, which outperformed previous logo detectors trained on synthetic data. We also provide a practical implementation for object fetching on a robot, which uses a Kinect and the logo detector to identify the object the human user requested. Tests on this robot indicate promising results, despite not using any real world photos for training.