Is honesty the best policy for mismatched partners? Aligning multi-modal affordances of a social robot: An opinion paper

Huang, Guanyu; Moore, Roger K.

doi:10.3389/frvir.2022.1020169

Cited by 3 publications

(3 citation statements)

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“…In the interaction, people may feel very disappointed and fall into the habitability gap when they find out that the robot cannot perform as it appears to be ( Moore, 2017 ). Thus, it can be said that a robot’s affordances need to be appropriately designed to represent explicitly its capabilities and to shape users’ expectations of it ( Huang and Moore, 2022 ).…”

Section: Background and Related Workmentioning

confidence: 99%

Freedom comes at a cost?: An exploratory study on affordances’ impact on users’ perception of a social robot

Huang,

Moore

2024

Front. Robot. AI

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Along with the development of speech and language technologies, the market for speech-enabled human-robot interactions (HRI) has grown in recent years. However, it is found that people feel their conversational interactions with such robots are far from satisfactory. One of the reasons is the habitability gap, where the usability of a speech-enabled agent drops when its flexibility increases. For social robots, such flexibility is reflected in the diverse choice of robots’ appearances, sounds and behaviours, which shape a robot’s ‘affordance’. Whilst designers or users have enjoyed the freedom of constructing a social robot by integrating off-the-shelf technologies, such freedom comes at a potential cost: the users’ perceptions and satisfaction. Designing appropriate affordances is essential for the quality of HRI. It is hypothesised that a social robot with aligned affordances could create an appropriate perception of the robot and increase users’ satisfaction when speaking with it. Given that previous studies of affordance alignment mainly focus on one interface’s characteristics and face-voice match, we aim to deepen our understanding of affordance alignment with a robot’s behaviours and use cases. In particular, we investigate how a robot’s affordances affect users’ perceptions in different types of use cases. For this purpose, we conducted an exploratory experiment that included three different affordance settings (adult-like, child-like, and robot-like) and three use cases (informative, emotional, and hybrid). Participants were invited to talk to social robots in person. A mixed-methods approach was employed for quantitative and qualitative analysis of 156 interaction samples. The results show that static affordance (face and voice) has a statistically significant effect on the perceived warmth of the first impression; use cases affect people’s perceptions more on perceived competence and warmth before and after interactions. In addition, it shows the importance of aligning static affordance with behavioural affordance. General design principles of behavioural affordances are proposed. We anticipate that our empirical evidence will provide a clearer guideline for speech-enabled social robots’ affordance design. It will be a starting point for more sophisticated design guidelines. For example, personalised affordance design for individual or group users in different contexts.

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Section: Background and Related Workmentioning

confidence: 99%

Freedom comes at a cost?: An exploratory study on affordances’ impact on users’ perception of a social robot

Huang,

Moore

2024

Front. Robot. AI

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“…Notably, while developers may well want human-robot interaction to be as natural as human interaction, this does not mean that every aspect of the robot has to be as human-like as possible. The design of robots’ behaviours and affordances is not only a technical but also an ethical issue ( Hildt, 2021 ; Huang & Moore, 2022 ). For example, people could react negatively to a robot’s deceptive praise ( Ham & Midden, 2014 ).…”

Section: Evaluation Of Rallmentioning

confidence: 99%

Using social robots for language learning: are we there yet?

Huang

Moore

2023

Journal of China Computer-Assisted Language Learning

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Along with the development of speech and language technologies and growing market interest, social robots have attracted more academic and commercial attention in recent decades. Their multimodal embodiment offers a broad range of possibilities, which have gained importance in the education sector. It has also led to a new technology-based field of language education: robot-assisted language learning (RALL). RALL has developed rapidly in second language learning, especially driven by the need to compensate for the shortage of first-language tutors. There are many implementation cases and studies of social robots, from early government-led attempts in Japan and South Korea to increasing research interests in Europe and worldwide. Compared with RALL used for English as a foreign language (EFL), however, there are fewer studies on applying RALL for teaching Chinese as a foreign language (CFL). One potential reason is that RALL is not well-known in the CFL field. This scope review paper attempts to fill this gap by addressing the balance between classroom implementation and research frontiers of social robots. The review first introduces the technical tool used in RALL, namely the social robot, at a high level. It then presents a historical overview of the real-life implementation of social robots in language classrooms in East Asia and Europe. It then provides a summary of the evaluation of RALL from the perspectives of L2 learners, teachers and technology developers. The overall goal of this paper is to gain insights into RALL’s potential and challenges and identify a rich set of open research questions for applying RALL to CFL. It is hoped that the review may inform interdisciplinary analysis and practice for scientific research and front-line teaching in future.

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“…And, if yes, what does that mean for potential error management in HRI? If priors shared between interlocutors matter (Huang and Moore, 2022;Moore, 2022), how does the aligning of interactive affordances help to increase the system's capacity to deal with errors? Moreover, errors can affect people's perception of a robot's trustworthiness and reliability (e.g., Washburn et al, 2020b), as well as their acceptance and willingness to cooperate in HRI (e.g., Salem et al, 2015).…”

Section: Wanted: a Taxonomy Of Conversational Failures In Hrimentioning

confidence: 99%

Working with troubles and failures in conversation between humans and robots: workshop report

Förster,

Romeo,

Holthaus

et al. 2023

Front. Robot. AI

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This paper summarizes the structure and findings from the first Workshop on Troubles and Failures in Conversations between Humans and Robots. The workshop was organized to bring together a small, interdisciplinary group of researchers working on miscommunication from two complementary perspectives. One group of technology-oriented researchers was made up of roboticists, Human-Robot Interaction (HRI) researchers and dialogue system experts. The second group involved experts from conversation analysis, cognitive science, and linguistics. Uniting both groups of researchers is the belief that communication failures between humans and machines need to be taken seriously and that a systematic analysis of such failures may open fruitful avenues in research beyond current practices to improve such systems, including both speech-centric and multimodal interfaces. This workshop represents a starting point for this endeavour. The aim of the workshop was threefold: Firstly, to establish an interdisciplinary network of researchers that share a common interest in investigating communicative failures with a particular view towards robotic speech interfaces; secondly, to gain a partial overview of the “failure landscape” as experienced by roboticists and HRI researchers; and thirdly, to determine the potential for creating a robotic benchmark scenario for testing future speech interfaces with respect to the identified failures. The present article summarizes both the “failure landscape” surveyed during the workshop as well as the outcomes of the attempt to define a benchmark scenario.

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Is honesty the best policy for mismatched partners? Aligning multi-modal affordances of a social robot: An opinion paper

Cited by 3 publications

References 33 publications

Freedom comes at a cost?: An exploratory study on affordances’ impact on users’ perception of a social robot

Freedom comes at a cost?: An exploratory study on affordances’ impact on users’ perception of a social robot

Using social robots for language learning: are we there yet?

Working with troubles and failures in conversation between humans and robots: workshop report

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