Effortless Polite Telepresence using Intention Recognition

Abstract

Telepresence technology creates the opportunity for people that were traditionally left out of the workforce to work remotely. In the service industry, a pool of novice remote workers could teleoperate robots to perform short work stints to fill in the gaps left by the dwindling workforce. A hurdle is that consistently talking appropriately and politely imposes a severe mental burden on such novice operators and the quality of the service may suffer. In this study, we propose a teleoperation support system that lets novice remote workers talk freely without considering appropriateness and politeness while maintaining the quality of the service. The proposed system exploits intent recognition to transform casual utterances into predefined appropriate and polite utterances. We conducted a within-subject user study where 23 participants played the role of novice remote operators controlling a guardsman robot in charge of monitoring customers’ behaviors. We measured the workload with and without using the proposed support system using NASA task load index questionnaires. The workload was significantly lower ( p < .001) when using the proposed support system ( M = 46.07, SD = 14.36) than when not using it ( M = 62.74, SD = 12.70). The effect size was large  (Cohen’s d = 1.23).