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

Sirithunge, Chapa; Jayasekara, A. G. Buddhika P.; Chandima, D. P.

doi:10.1155/2021/3648479

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…36.5% of the participants preferred interacting with a robot during a relaxed occasion while 49.2% were uncertain of their decision. Possible reasons for this uncertainty were discussed in [21]. According to [21], the internal state of mind of a person largely contributes to their decision to interact with a robot.…”

Section: Resultsmentioning

confidence: 99%

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How Do We Trust Our Robots?

Sirithunge,

Dahn

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Trust plays the most important role in accepting social robots in our communities, especially in an era where robots take part in daily activities in close distance with humans. While some communities accept the presence of robots in their surrounding, others are less open to them. Furthermore, this acceptance depends on a large number of factors which are specific to the communities as well as the deployed robots. We conducted a survey to explore the aspects which people would consider if they had an option to deploy a robot at home and assign responsibilities to it. The study will help direct future researches on embodied intelligence in robots towards a more human-accepted level. The paper presents a human study conducted to evaluate the trust between humans and robots. We present the feedback we received from the participants to assess the level of trust participants have on their robots and their personal preferences upon the abilities of robots. Human responses and their decisions observed during the study are analyzed and critical observations are highlighted. From the analysis we derive a set of guidelines to improve human trust in future robots by adjusting their humanlike social behaviors.

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Section: Resultsmentioning

confidence: 99%

“…Possible reasons for this uncertainty were discussed in [21]. According to [21], the internal state of mind of a person largely contributes to their decision to interact with a robot. Therefore, a robot has to be able to understand if a situation is suitable for interaction prior to a conversation.…”

Section: Resultsmentioning

confidence: 99%

How Do We Trust Our Robots?

Sirithunge,

Dahn

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…To participate in collaborations with people, robots must not only see and talk with people but also use the conventions of conversation to connect with their human counterparts (Sirithunge et al, 2021 ). A group conversation is a form of conversation in which three or more participants talk to each other about a topic on an equal footing.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of interactive communication between Pepper robot, patients with schizophrenia, and healthy persons

et al. 2022

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Background: Expressing enjoyment when conversing with healthcare robots is an opportunity to enhance the value of human robots with interactive capabilities. In clinical practice, it is common to find verbal dysfunctions in patients with schizophrenia. Thus, interactive communication characteristics may vary between Pepper robot, persons with schizophrenia, and healthy persons. Objective: Two case studies aimed to describe the characteristics of interactive communications, 1) between Pepper as a healthcare robot and two patients with schizophrenia, and 2) between Pepper as a healthcare robot and two healthy persons. Case Report: The “Intentional Observational Clinical Research Design” was used to collect data. Using audio-video technology, the conversational interactions between the four participants with the Pepper healthcare robot were recorded. Their interactions were observed, with significant events noted. After their interactions, the four participants were interviewed regarding their experience and impressions of interacting with the Pepper healthcare robot. Audio-video recordings were analyzed following the analysis and interpretation protocol, and the interview data were transcribed, analyzed, and interpreted. Discussion: There were similarities and differences in the interactive communication characteristics between the Pepper robot and the two participants with schizophrenia and between Pepper and the two healthy participants. The similarities were experiences of human enjoyment while interacting with the Pepper robot. This enjoyment was enhanced with the expectancy of the Pepper robot as able to entertain, and possessing interactive capabilities, indicating two-way conversational abilities. However, different communicating characteristics were found between the healthy participants’ impressions of the Pepper robot and the participants with schizophrenia. Healthy participants understood Pepper to be an automaton, with responses to questions often constrained and, on many occasions, displaying inaccurate gaze. Conclusion: Pepper robot showed capabilities for effective communication pertaining to expressing enjoyment. The accuracy and appropriateness of gaze remained a critical characteristic regardless of the situation or occasion with interactions between persons with schizophrenia, and between healthy persons. It is important to consider that in the future, for effective use of healthcare robots with multiple users, improvements in the areas of the appropriateness of gaze, response time during the conversation, and entertaining functions are critically observed.

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“…For instance, Chai et al [30] model a student's willingness to study a subject based on the Theory of Reasoned Action. Pic ¸arra et al [31] and Sirithunge et al [32] use the theoretical framework provided by the Theory of Planned Behaviour to design context-aware social robots. These theories are also applied in a variety of other fields like organisational behaviour management [33], online education [34], etc.…”

Section: Plos Onementioning

confidence: 99%

From multimodal features to behavioural inferences: A pipeline to model engagement in human-robot interactions

Joshi,

Malavalli,

Rao

2023

PLoS ONE

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Modelling the engaging behaviour of humans using multimodal data collected during human-robot interactions has attracted much research interest. Most methods that have been proposed previously predict engaging behaviour directly from multimodal features, and do not incorporate personality inferences or any theories of interpersonal behaviour in human-human interactions. This work investigates whether personality inferences and attributes from interpersonal theories of behaviour (like attitude and emotion) further augment the modelling of engaging behaviour. We present a novel pipeline to model engaging behaviour that incorporates the Big Five personality traits, the Interpersonal Circumplex (IPC), and the Triandis Theory of Interpersonal Behaviour (TIB). We extract first-person vision and physiological features from the MHHRI dataset and predict the Big Five personality traits using a Support Vector Machine. Subsequently, we empirically validate the advantage of incorporating personality in modelling engaging behaviour and present a novel method that effectively uses the IPC to obtain scores for a human’s attitude and emotion from their Big Five traits. Finally, our results demonstrate that attitude and emotion are correlates of behaviour even in human-robot interactions, as suggested by the TIB for human-human interactions. Furthermore, incorporating the IPC and the Big Five traits helps generate behavioural inferences that supplement the engaging behaviour prediction, thus enriching the pipeline. Engagement modelling has a wide range of applications in domains like online learning platforms, assistive robotics, and intelligent conversational agents. Practitioners can also use this work in cognitive modelling and psychology to find more complex and subtle relations between humans’ behaviour and personality traits, and discover new dynamics of the human psyche. The code will be made available at: https://github.com/soham-joshi/engagement-prediction-mhhri.

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

Cited by 6 publications

References 35 publications

How Do We Trust Our Robots?

How Do We Trust Our Robots?

Characteristics of interactive communication between Pepper robot, patients with schizophrenia, and healthy persons

From multimodal features to behavioural inferences: A pipeline to model engagement in human-robot interactions

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