AUTHOR=Alotaibi Sara Bader , Manimurugan S. 

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

JOURNAL=Frontiers in Sustainable Cities

VOLUME=Volume 4 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2022.1076101

DOI=10.3389/frsc.2022.1076101

ISSN=2624-9634

ABSTRACT=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 that can communicate 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. This paper investigates techniques to uncover proposals for explicitly deploying Artificial Intelligence (AI) and robots in a smart city environment. This paper emphases on developing a humanoid robotic system for social interaction using the Internet of Robot 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. The tasks required to move around the surroundings and respond to queries from the audience are controlled by robotic interaction control, which has a straight connection between the sensing units and detectors. DIL combines the idea of deep learning utilizing neural networks and reinforcement learning techniques to enable the interaction of humanoid robots. 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%.