Active Object Learning for intelligent social robots

Abstract

The applications of social robots have continued to rise in number over the years: there are now mainly used in nursing homes and hospitals, performing different tasks like assistance, physical and cognitive rehabilitation, and supervision. However, robots’ ability to learn from humans remains a problem to be solved. This paper presents the development of an Active Object Learning architecture in which the robot can identify unknown objects and autonomously initiate the learning process, supported by user interactions. We study the feasibility of generating a real-time dataset, including techniques for reducing image repetition and online classification model training. The work considers the analysis of machine learning metrics such as F1-Score, accuracy, confidence, and hit rate as the robot learns new objects. The accuracy of the classifier remains constant as the number of classes increases, indicating that the regularisation methods reduce overfitting and decrease the generalisation gap. The results prove that the robot can perform the classifications correctly and differentiate between known and unknown objects. The paper also includes a case study to demonstrate the feasibility of the proposed system integrated into a real social robot.