Remote HRI: a Methodology for Maintaining COVID-19 Physical Distancing and Human Interaction Requirements in HRI Studies

Abstract

Observing how humans and robots interact is an integral part of understanding how they can effectively coexist. This ability to undertake these observations was taken for granted before the COVID-19 pandemic restricted the possibilities of performing HRI study-based interactions. We explore the problem of how HRI research can occur in a setting where physical separation is the most reliable way of preventing disease transmission. We present the results of an exploratory experiment that suggests Remote-HRI (R-HRI) studies may be a viable alternative to traditional face-to-face HRI studies. An R-HRI study minimizes or eliminates in-person interaction between the experimenter and the participant and implements a new protocol for interacting with the robot to minimize physical contact. Our results showed that participants interacting with the robot remotely experienced a higher cognitive workload, which may be due to minor cultural and technical factors. Importantly, however, we also found that whether participants interacted with the robot in-person (but socially distanced) or remotely over a network, their experience, perception of, and attitude towards the robot were unaffected.

Keywords: COVID-19 pandemic; HRI; Remote interaction; Remote-HRI; Social robots.

Fig. 1

The Zenbo intelligent robot (ASUS)

Fig. 2

Q-Q Plot visual test for normality for the NASA TLX inventory

Fig. 3

Q-Q plots of a TAM2 IU subscale; b TAM2 PU subscale and; c TAM2 PEU subscale showing that the subscale data may fit a normal distribution, with minimum R²value of 0.871, 2 (c), and maximum R²value of 0.9861, 2 (b)

Fig. 4

Q-Q Plot visual normality test for al NASA TLX subscales

Fig. 5

Q-Q Plot of UEQ a Pragmatic subscale and b Hedonic subscale showing that the data seems to follow a normal distribution