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

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change how much people are willing to take risks. This study further develops our understanding of the relationship between human risk-taking behaviour, the physiological responses by the user, and the intensity of the tactile interaction with a social robot. We used data collected with physiological sensors during the playing of a risk-taking game (the Balloon Analogue Risk Task, or BART). The results of a mixed-effects model were used as a baseline to predict risk-taking propensity from physiological measures, and these results were further improved through the use of two machine learning techniques-support vector regression (SVR) and multi-input convolutional multihead attention (MCMA)-to achieve low-latency risk-taking behaviour prediction during human-robot tactile interaction. The performance of the models was evaluated based on mean absolute error (MAE), root mean squared error (RMSE), and R squared score (R-2), which obtained the optimal result with MCMA yielding an MAE of 3.17, an RMSE of 4.38, and an R-2 of 0.93 compared with the baseline of 10.97 MAE, 14.73 RMSE, and 0.30 R-2. The results of this study offer new insights into the interplay between physiological data and the intensity of risk-taking behaviour in predicting human risk-taking behaviour during human-robot tactile interactions. This work illustrates that physiological activation and the intensity of tactile interaction play a prominent role in risk processing during human-robot tactile interaction and demonstrates that it is feasible to use human physiological data and behavioural data to predict risk-taking behaviour in human-robot tactile interaction.

Automated summary

Touch has a significant impact on human interactions, and this study explores its importance in human-robot interactions. The research predicts human risk-taking behavior during human-robot tactile interactions by using physiological data collected with sensors. The combination of support vector regression (SVR) and multi-input convolutional multihead attention (MCMA) enables low-latency risk-taking behavior prediction. The study reveals the interplay between physiological data and the intensity of tactile interaction in predicting human risk-taking behavior, highlighting the feasibility of using human physiological and behavioral data in predicting risk-taking behavior during human-robot tactile interactions.