Exploring the Causal Modeling of Human-Robot Touch Interaction
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The growing emergence of socially assistive robots in our daily lives inevitably entails such interactions as touch and hug between robots and humans. Therefore, derivation of robust models for such physical interactions to enable robots to perform them in naturalistic fashion is highly desirable. In this study, we investigated whether it was possible to realize distinct patterns of different touch interactions that were general representations of their respective types. For this purpose, we adapted three touch interaction paradigms and asked human subjects to perform them on a mannequin that was equipped with a touch sensor on its torso. We then applied Wiener-Granger causality on the time series of activated channels of this touch sensor that were common (per touch paradigm) among all participants. The analyses of these touch time series suggested that different types of touch can be quantified in terms of causal association between sequential steps that form the variation information among their patterns. These results hinted at the potential utility of such generalized touch patterns for devising social robots with robust causal models of naturalistic touch behaviour for their human-robot touch interactions.