A Survey on Artificial Neural Networks in Human-Robot Interaction

Area of Science:

• Artificial Intelligence
• Robotics
• Human-Computer Interaction

Background:

• Artificial neural networks (ANNs) are biologically inspired computational models.
• ANNs excel at learning from data and generalizing to new scenarios.
• Their application in human-robot interaction (HRI) is a rapidly developing field.

Purpose of the Study:

• To review the current state of ANNs in HRI.
• To highlight opportunities and challenges associated with ANNs in HRI.
• To discuss future research directions for ANNs in HRI.

Main Methods:

• Review of existing research on ANNs applied to HRI.
• Analysis of ANNs' capabilities in gesture recognition, natural language understanding, and environmental adaptation.
• Examination of ANNs' role in enhancing robot autonomy and decision-making.

Main Results:

• ANNs enable more natural and intuitive HRI through advanced perception and adaptation.
• ANNs can improve robot autonomy by facilitating learning from human interactions.
• Key challenges include substantial data requirements, explainability issues, and potential algorithmic bias.

Conclusions:

• ANNs offer significant potential for advancing HRI by creating more interactive and intelligent robotic systems.
• Addressing challenges in data, explainability, and bias is crucial for the successful integration of ANNs in HRI.
• Future research should focus on developing robust, interpretable, and unbiased ANNs for sophisticated HRI applications.