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Behavioural Models of Risk-Taking in Human-Robot Tactile Interactions.

Qiaoqiao Ren1, Yuanbo Hou2, Dick Botteldooren2

  • 1AIRO-IDLab, Faculty of Engineering and Architecture, Ghent University-Imec, Technologiepark 126, 9052 Gent, Belgium.

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Summary
This summary is machine-generated.

Touch intensity with social robots influences human risk-taking. Physiological responses and tactile interaction intensity can predict risk-taking behavior during human-robot interaction, enhancing prediction accuracy.

Keywords:
behaviour modelhuman–robot tactile interactionnon-verbal interactionrisk-taking behaviour

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Area of Science:

  • Human-robot interaction
  • Affective computing
  • Physiological computing

Background:

  • Tactile interaction significantly influences human behavior and social dynamics.
  • Previous research indicated that tactile intensity with robots affects human risk-taking propensity.
  • Understanding the physiological underpinnings of this interaction is crucial for designing effective social robots.

Purpose of the Study:

  • To investigate the relationship between human risk-taking behavior, physiological responses, and tactile interaction intensity with a social robot.
  • To develop and evaluate machine learning models for predicting risk-taking behavior during human-robot tactile interaction.
  • To identify key physiological and behavioral indicators of risk processing in human-robot interactions.

Main Methods:

  • Collected physiological sensor data from participants playing the Balloon Analogue Risk Task (BART) during human-robot tactile interaction.
  • Utilized a mixed-effects model as a baseline for predicting risk-taking propensity from physiological measures.
  • Employed machine learning techniques, specifically Support Vector Regression (SVR) and Multi-input Convolutional Multihead Attention (MCMA), to improve prediction accuracy and latency.

Main Results:

  • The MCMA model significantly outperformed the baseline mixed-effects model in predicting risk-taking behavior.
  • MCMA achieved a Mean Absolute Error (MAE) of 3.17, Root Mean Squared Error (RMSE) of 4.38, and R-squared (R2) of 0.93, compared to the baseline's MAE of 10.97, RMSE of 14.73, and R2 of 0.30.
  • Physiological activation and tactile interaction intensity were identified as prominent factors in risk processing during human-robot interaction.

Conclusions:

  • Physiological data, combined with behavioral and tactile interaction data, can accurately predict human risk-taking behavior in human-robot interactions.
  • The study demonstrates the feasibility of using machine learning for low-latency prediction of risk-taking behavior.
  • Findings offer insights into the interplay between physiological responses, tactile interaction, and risk processing, informing the design of more intuitive and responsive social robots.