You’re Doing It Wrong! Studying Unexpected Behaviors in Child-Robot Interaction

Abstract

Abstract. We present a study on the impact of unexpected robot behaviors on the perception of a robot by children and their subsequent engagement in a playful interaction based on a novel "domino" task. We propose an original analysis methodology which blends behavioral cues and reported phenomenological perceptions into a compound index. While we found only a limited recognition of the different misbehaviors of the robot that we attribute to the age of the child participants (4-5 years old), interesting findings include a sustained engagement level, an unexpectedly low level of attribution of higher cognitive abilities and a negative correlation between anthropomorphic projections and actual behavioral engagement.

Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

“…Various definitions exist for the terms “failure,” “error,” and “fault.” In line with (Laprie, 1995 ; Carlson and Murphy, 2005 ; Steinbauer, 2013 ; Brooks, 2017 ), we adopted terminology in which failure refers to “a degraded state of ability which causes the behavior or service being performed by the system to deviate from the ideal, normal, or correct functionality” (Brooks, 2017 ). This definition includes both perceived failures, unexpected behavior and actual failures, which is consistent with findings that suggest that intentional yet unexpected or incoherent behaviors are sometimes interpreted as erroneous (Short et al, 2010 ; Lemaignan et al, 2015 ). Failures result from one or more errors , which refer to system states (electrical, logical, or mechanical) that can lead to a failure.…”

“…User perceptions of the robot that have been evaluated in erroneous situations include the robot's perceived agency (Lemaignan et al, 2015 ; van der Woerdt and Haselager, 2017 ), predictability (van der Woerdt and Haselager, 2017 ), apologeticness (Shiomi et al, 2013 ), moral accountability (Kahn et al, 2012 ), friendliness (Groom et al, 2010 ; Shiomi et al, 2013 ; Kim et al, 2017 ), propensity to damage (van der Woerdt and Haselager, 2017 ), trustworthiness (Gompei and Umemuro, 2015 ; Brooks et al, 2016 ; Hamacher et al, 2016 ; Rossi et al, 2017a ; Sarkar et al, 2017 ; van der Woerdt and Haselager, 2017 ; Kwon et al, 2018 ), likeability (Groom et al, 2010 ; Salem et al, 2013 ; Bajones et al, 2016 ; Engelhardt and Hansson, 2017 ; Mirnig et al, 2017 ; Sarkar et al, 2017 ), reliability (Short et al, 2010 ; Salem et al, 2015 ), familiarity (Gompei and Umemuro, 2015 ), anthropomorphism (Lee et al, 2010 ; Salem et al, 2013 , 2015 ; Lemaignan et al, 2015 ; Mubin and Bartneck, 2015 ; Mirnig et al, 2017 ; Sarkar et al, 2017 ), animacy (Engelhardt and Hansson, 2017 ; Sarkar et al, 2017 ), technical competence (Groom et al, 2010 ; Short et al, 2010 ; Desai et al, 2013 ; Salem et al, 2015 ; Brooks et al, 2016 ; Engelhardt and Hansson, 2017 ; Sarkar et al, 2017 ), dependability (Brooks et al, 2016 ), intelligence (Mubin and Bartneck, 2015 ; Salem et al, 2015 ; Bajones et al, 2016 ; Engelhardt and Hansson, 2017 ; Mirnig et al, 2017 ; Sarkar et al, 2017 ), belligerence (Groom et al, 2010 ) and safety (Salem et al, ...…”

“…Participants varied in age, however most studies were primarily implemented on younger adults. One study evaluated children (Lemaignan et al, 2015 ); none focused on elderly participants above the age of 75. With the exception of seven studies, the distribution between male and female participants was relatively equal (more equal than 60–40%).…”

“…NAO was by far the most commonly used robot (Gehle et al, 2015 ; Giuliani et al, 2015 ; Gompei and Umemuro, 2015 ; Mirnig et al, 2015 , 2017 ; Engelhardt and Hansson, 2017 ; van der Woerdt and Haselager, 2017 ; Lucas et al, 2018 ), however several other off-the-shelf solutions were used, including BIRON (Spexard et al, 2008 ), Kuka youBots (Knepper et al, 2015 ), iRobot ATRV-JR (Desai et al, 2012 ), Robovie-mR2 (Shiomi et al, 2013 ), Snackbot (Lee et al, 2010 ), and Baxter (Adubor et al, 2017 ; Sarkar et al, 2017 ). Several systems were custom made for the purpose of the research (Yasuda and Matsumoto, 2013 ; Lohan et al, 2014 ; Lemaignan et al, 2015 ; Mubin and Bartneck, 2015 ). About half of the 52 studies used humanoid robots [robots that possess some human-like features (Walters et al, 2008 )], and half used mechanoid robots [robots that are machine-like in appearance (Walters et al, 2008 )].…”

Building successful long child-robot interactions in a learning context

“…Using questionnaires with children can lead to contradictions between the actual behavior of the child during interaction and his answers during the interview [14]. One reason is that children have the tendency to try to please the experimenter, rather than answer truthfully to survey questions [2].…”