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. 2024 May 22;24(11):3311.
doi: 10.3390/s24113311.

Implementation of Engagement Detection for Human-Robot Interaction in Complex Environments

Sin-Ru Lu  1 , Jia-Hsun Lo  1 , Yi-Tian Hong  1 , Han-Pang Huang  1
Affiliations

Implementation of Engagement Detection for Human-Robot Interaction in Complex Environments

Sin-Ru Lu et al. Sensors (Basel). .

Abstract

This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human-robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system's efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor's intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system's performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.

Keywords: action recognition; cognitive system; engagement; human behaviors; human–robot interaction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The four layers in HRI. The design of robots should satisfy all design conditions from safety to sociability.
Figure 2
Figure 2
Robot cognitive system. The system can be divided into three parts: engagement model, intention model, and HRI model. In engagement model, head poses, eye angles, and actions are analyzed by engagement HMM to generate appropriate state of engagement. After acquiring the output from engagement HMM, intention model is utilized to identify the intention state of targets. To provide better interaction experience, HRI model is included as a policy system of HRI.
Figure 3
Figure 3
Structure of the gaze tracker. At first, the 2D eye region is processed by eye gaze detector. To restore the eye region image, SRWGAN is included in our model. Finally, a CNN-based classifier is trained to obtain the eye angles.
Figure 4
Figure 4
Model of emotion classifier.
Figure 5
Figure 5
HRI model with google DialogFlow.
Figure 6
Figure 6
Engagement equation.
Figure 7
Figure 7
The hardware of Mobi.
Figure 8
Figure 8
Scenario of our experiment.
Figure 9
Figure 9
The people interact with Mobi.
Figure 10
Figure 10
The engagement state of subject.
Figure 11
Figure 11
Engagement comfort index changes with time. At 2 s, the index reaches −0.2 such that the start of HRI is detected. Before 10 s, the subject continues staring at Mobi, making the index keep declining to −1.0.
Figure 12
Figure 12
The subjects interact with Mobi.
Figure 13
Figure 13
Mobi identified the interactors using engagement comfort index.
Figure 14
Figure 14
Mobi helped the customer to find the goods.
See this image and copyright information in PMC

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