Argumentation based coaching of an industrial robotic arm

Since the advent of the first industrial revolution, the need for machines that would help to increase production in order to fulfill market demands has increased exponentially. Industrial robots have sparked a lot of attention since then. In order to cope with industrial needs, engineers and machine designers have endeavored to construct machines that would work on the kinematics inspired by the human arm. With the developments in technology, industrial robotic arms have changed over time. Although the initial models were more hydraulic and hardwire driven, the recent robotic arms incorporate highly sophisticated mechanics, electronics, and software. With the dawn of the Fourth Industrial Revolution, industries have increased their technology benchmark and are in need of smart technology that can learn, infer, and explain their behavior. This has expanded the research in the Human Machine Interaction domain where scientists have managed to propose such systems where interacting with industrial machines has become easier. Building automation systems through no code or low code approaches has further alleviated the technology benchmarks. In this Master’s dissertation, we propose an approach under the shadow of the Human Machine Interaction domain to coach an industrial robotic arm through the PRUDENS interface that facilitates machine coaching through argumentation and machine-learning theories, which appear to be useful in monitoring the machine’s behavior and guiding it to adapt itself under exceptional settings. PRUDENS is a software tool that has been developed by the Computational Cognition Lab of the Open University of Cyprus led by Dr. Loizos Michael. We implement a real-time human-robot interaction system that facilitates machine coaching within industrial boundaries, in addition to discussing recent trends in the human-robot interaction domain and the implications of AI, ML, and argumentation techniques on it.