Exploring the Role of Trust and Expectations in CRI Using In-the-Wild Studies

Paulina Zguda,Bartlomiej Sniezynski,Anna Kołota,Bipin Indurkhya,Gentiane Venture

doi:10.3390/electronics10030347

Abstract

Studying interactions of children with humanoid robots in familiar spaces in natural contexts has become a key issue for social robotics. To fill this need, we conducted several Child–Robot Interaction (CRI) events with the Pepper robot in Polish and Japanese kindergartens. In this paper, we explore the role of trust and expectations towards the robot in determining the success of CRI. We present several observations from the video recordings of our CRI events and the transcripts of free-format question-answering sessions with the robot using the Wizard-of-Oz (WOZ) methodology. From these observations, we identify children’s behaviors that indicate trust (or lack thereof) towards the robot, e.g., challenging behavior of a robot or physical interactions with it. We also gather insights into children’s expectations, e.g., verifying expectations as a causal process and an agency or expectations concerning the robot’s relationships, preferences and physical and behavioral capabilities. Based on our experiences, we suggest some guidelines for designing more effective CRI scenarios. Finally, we argue for the effectiveness of in-the-wild methodologies for planning and executing qualitative CRI studies.

Highlights

Due to a growing number of social robots being deployed to interact with children, both neurotypical and those with special needs, the field of Child–Robot Interaction (CRI ) is facing many research challenges
Trust is a basic foundation of building human–human relationships [79]: it facilitates seamless and natural interactions. It is important in human–robot and Child– Robot Interaction (CRI), especially as social robots are becoming more commonplace and are engaging with people in a variety of roles: companion, caregiver, guide, assistant, etc
We aimed to highlight the role of trust and expectations in CRI

Summary

Introduction

Due to a growing number of social robots being deployed to interact with children, both neurotypical and those with special needs, the field of Child–Robot Interaction (CRI ) is facing many research challenges. One challenge is to design anthropomorphic manipulators that show human-like behaviors. Several machine-learning algorithms are being deployed [1,2]. Another is to extract emotions from motions and gestures, for which recurrent neural networks have been employed [3], and from signals collected using wearable wireless sensors, for which unsupervised machine learning approaches have been proposed [4]. Unsupervised learning is applied in [5], where deep-learning auto-encoders are used for recognizing human activity and engagement. II “How do you talk to a dog” poem. Caterpillar” poems “Paprika” dancing IV Drawing V Q&A Session VI

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