Literature DB >> 33680073

An Evaluation of Human Conversational Preferences in Social Human-Robot Interaction.

Chapa Sirithunge1, A G Buddhika P Jayasekara1, D P Chandima1.   

Abstract

To generate context-aware behaviors in robots, robots are required to have a careful evaluation of its encounters with humans. Unwrapping emotional hints in observable cues in an encounter will improve a robot's etiquettes in a social encounter. This article presents an extended human study conducted to examine how several factors in an encounter influence a person's preferences upon an interaction at a particular moment. We analyzed the nature of conversation preferred by a user considering the type of conversation a robot could have with its user, having the interaction initiated by the robot itself. We took an effort to explore how such preferences differ as the factors present in the surrounding alter. A social robot equipped with the capability to initiate a conversation is deployed to conduct the study by means of a wizard-of-oz (WoZ) experiment. During this study, conversational preferences of users could vary from "no interaction at all" to a "long conversation." We changed three factors in an encounter which can be different from each other in each circumstance: the audience or outsiders in the environment, user's task, and the domestic area in which the interaction takes place. Conversational preferences of users within the abovementioned conditions were analyzed in a later stage, and critical observations are highlighted. Finally, implications that could be helpful in shaping future social human-robot encounters were derived from the analysis of the results.
Copyright © 2021 Chapa Sirithunge et al.

Entities:  

Year:  2021        PMID: 33680073      PMCID: PMC7925063          DOI: 10.1155/2021/3648479

Source DB:  PubMed          Journal:  Appl Bionics Biomech        ISSN: 1176-2322            Impact factor:   1.781


  1 in total

1.  How Service Robots Can Improve Workplace Experience: Camaraderie, Customization, and Humans-in-the-Loop.

Authors:  Yao-Lin Tsai; Chinmay Wadgaonkar; Bohkyung Chun; Heather Knight
Journal:  Int J Soc Robot       Date:  2022-06-28       Impact factor: 3.802

  1 in total

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