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A method based on interpretable machine learning for recognizing the intensity of human engagement intention.

Jian Bi¹, Fang-Chao Hu¹, Yu-Jin Wang¹

¹College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China.

Scientific Reports

|February 13, 2023

Summary

Social robots can now better understand human engagement by analyzing the intensity of human engagement intention (IHEI). This new approach helps robots identify the primary interaction person in multi-person settings for more natural human-robot interaction.

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Area of Science:

Robotics
Human-Computer Interaction
Artificial Intelligence

Background:

Social robots require enhanced perception for natural human interaction, particularly in multi-person scenarios.
Current research often identifies interaction intention but lacks analysis of its intensity (IHEI).
Distinguishing the primary interaction person is crucial for effective human-robot collaboration.

Purpose of the Study:

To propose and validate an approach for recognizing the intensity of human engagement intention (IHEI).
To enable social robots to differentiate individuals' engagement levels in complex interaction settings.
To improve the precision and naturalness of human-robot interactions.

Main Methods:

Utilized four categories of visual features: line of sight, head pose, distance, and human expression.
Applied a CatBoost-based machine learning model to train a classifier for predicting IHEI.
Conducted interpretability analysis on the trained model to understand feature associations.

Main Results:

The developed classifier effectively predicts the intensity of human engagement intention (IHEI).
Experimental results demonstrate the model's applicability in real-world human-robot interaction scenarios.
The interpretable model provides insights into the relationship between visual features and engagement intention.

Conclusions:

The proposed approach offers a robust method for recognizing IHEI in social robots.
This capability enhances robot social decision-making for more effective human-robot interaction.
The interpretable nature of the model facilitates deeper understanding and trust in robot behavior.