A Survey on Artificial Neural Networks in Human-Robot Interaction.

Abstract

Artificial neural networks (ANNs) have shown great potential in enhancing human-robot interaction (HRI). ANNs are computational models inspired by the structure and function of biological neural networks in the brain, which can learn from examples and generalize to new situations. ANNs can be used to enable robots to interact with humans in a more natural and intuitive way by allowing them to recognize human gestures and expressions, understand natural language, and adapt to the environment. ANNs can also be used to improve robot autonomy, allowing robots to learn from their interactions with humans and to make more informed decisions. However, there are also challenges to using ANNs in HRI, including the need for large amounts of training data, issues with explainability, and the potential for bias. This review explores the current state of research on ANNs in HRI, highlighting both the opportunities and challenges of this approach and discussing potential directions for future research. The AI contribution involves applying ANNs to various aspects of HRI, while the application in engineering involves using ANNs to develop more interactive and intuitive robotic systems.