Humanoid robotic system for social interaction using deep imitation learning in a smart city environment

Abstract

Introduction: A significant resource for understanding the prospects of smart development is the smart city initiatives created by towns all around the globe. Robots have changed from purely human-serving machines to machines communicating with humans through displays, voice, and signals. The humanoid robots are part of a class of sophisticated social robots. Humanoid robots can share and coexist with people and look similar to humans. Methods: This paper investigates techniques to uncover proposals for explicitly deploying Artificial Intelligence (AI) and robots in a smart city environment. This paper emphasis on providing a humanoid robotic system for social interaction using the Internet of Robotic Things-based Deep Imitation Learning (IoRT-DIL) in a smart city. In the context of the IoT ecosystem of linked intelligent devices and sensors ubiquitously embedded in everyday contexts, the IoRT standard brings together intelligent mobile robots. IoRT-DIL has been used to create a free mobility mode and a social interaction mode for the robot that can detect when people approach it with inquiries. In direct contact with the actuators and sensors, robotic interface control is responsible for guiding the robot as it navigates its environment and answers questions from the audience. Results and discussion: For the robots to function safely, they must be monitored and enforced by a central controller using Internet of Robotic Things (IoRT) technology in an emergency. DIL aims to facilitate robot-human interaction by integrating deep learning architectures based on Neural Networks (NN) and reinforced learning methods. DIL focuses on mimicking human learning or expertise presentation to govern robot behavior. The robot's interaction has been tracked in a smart city setting, and its real-time efficiency using DIL is 95%.