로봇의 고개를 움직이는 동작과 타이밍이 인간과 로봇의 상호작용에 미치는 효과

학위논문(석사) -- 서울대학교대학원 : 인문대학 협동과정 인지과학전공, 2023. 2. Sowon Hahn.In recent years, robots with artificial intelligence capabilities have become ubiquitous in our daily lives. As intelligent robots are interacting closely with humans, social abilities of robots are increasingly more important. In particular, nonverbal communication can enhance the efficient social interaction between human users and robots, but there are limitations of behavior expression. In this study, we investigated how minimal head movements of the robot influence human-robot interaction. We newly designed a robot which has a simple shaped body and minimal head movement mechanism. We conducted an experiment to examine participants' perception of robots different head movements and timing. Participants were randomly assigned to one of three movement conditions, head nodding (A), head shaking (B) and head tilting (C). Each movement condition included two timing variables, prior head movement of utterance and simultaneous head movement with utterance. For all head movement conditions, participants' perception of anthropomorphism, animacy, likeability and intelligence were higher compared to non-movement (utterance only) condition. In terms of timing, when the robot performed head movement prior to utterance, perceived naturalness was rated higher than simultaneous head movement with utterance. The findings demonstrated that head movements of the robot positively affects user perception of the robot, and head movement prior to utterance can make human-robot conversation more natural. By implementation of head movement and movement timing, simple shaped robots can have better social interaction with humans.최근 인공지능 로봇은 일상에서 흔하게 접할 수 있는 것이 되었다. 인간과의 교류가 늘어남에 따라 로봇의 사회적 능력은 더 중요해지고 있다. 인간과 로봇의 사회적 상호작용은 비언어적 커뮤니케이션을 통해 강화될 수 있다. 그러나 로봇은 비언어적 제스처의 표현에 제약을 갖는다. 또한 로봇의 응답 지연 문제는 인간이 불편한 침묵의 순간을 경험하게 한다. 본 연구를 통해 로봇의 고개 움직임이 인간과 로봇의 상호작용에 어떤 영향을 미치는지 알아보았다. 로봇의 고개 움직임을 탐구하기 위해 단순한 형상과 고개를 움직이는 구조를 가진 로봇을 새롭게 디자인하였다. 이 로봇을 활용하여 로봇의 머리 움직임과 타이밍이 참여자에게 어떻게 지각되는지 실험하였다. 참여자들은 3가지 움직임 조건인, 끄덕임 (A), 좌우로 저음 (B), 기울임 (C) 중 한 가지 조건에 무작위로 선정되었다. 각각의 고개 움직임 조건은 두 가지 타이밍(음성보다 앞선 고개 움직임, 음성과 동시에 일어나는 고개 움직임)의 변수를 갖는다. 모든 타입의 고개 움직임에서 움직임이 없는 조건과 비교하여 로봇의 인격화, 활동성, 호감도, 감지된 지능이 향상된 것을 관찰하였다. 타이밍은 로봇의 음성보다 고개 움직임이 앞설 때 자연스러움이 높게 지각되는 것으로 관찰되었다. 결과적으로, 로봇의 고개 움직임은 사용자의 지각에 긍정적인 영향을 주며, 앞선 타이밍의 고개 움직임이 자연스러움을 향상시키는 것을 확인하였다. 고개를 움직이는 동작과 타이밍을 통해 단순한 형상의 로봇과 인간의 상호작용이 향상될 수 있음을 본 연구를 통해 확인하였다.Chapter 1. Introduction 1 1.1. Motivation 1 1.2. Literature Review and Hypotheses 3 1.3. Purpose of Study 11 Chapter 2. Experiment 13 2.1. Methods 13 2.2. Results 22 2.3. Discussion 33 Chapter 3. Conclusion 35 Chapter 4. General Discussion 37 4.1. Theoretical Implications 37 4.2. Practical Implications 38 4.3. Limitations and Future work 39 References 41 Appendix 53 Abstract in Korean 55석

How Transparency Measures Can Attenuate Initial Failures of Intelligent Decision Support Systems

Owing to high functional complexity, trust plays a critical role for the adoption of intelligent decision support systems (DSS). Especially failures in initial usage phases might endanger trust since users are yet to assess the system’s capabilities over time. Since such initial failures are unavoidable, it is crucial to understand how providers can inform users about system capabilities to rebuild user trust. Using an online experiment, we evaluate the effects of recurring explanations and initial tutorials as transparency measures on trust. We find that recurring explanations are superior to initial tutorials in establishing trust in intelligent DSS. However, recurring explanations are only as effective as tutorials or the combination of both tutorials and recurring explanations in rebuilding trust after initial failures occurred. Our results provide empirical insights for the design of transparency mechanisms for intelligent DSS, especially those with high underlying algorithmic complexity or potentially high damage

Having The Right Attitude: How Attitude Impacts Trust Repair in Human-Robot Interaction,

Robot co-workers, like human co-workers, make mistakes that undermine trust. Yet, trust is just as important in promoting human–robot collaboration as it is in promoting human–human collaboration. In addition, individuals can significantly differ in their attitudes toward robots, which can also impact or hinder their trust in robots. To better understand how individual attitude can influence trust repair strategies, we propose a theoretical model that draws from the theory of cognitive dissonance. To empirically verify this model, we conducted a between-subjects experiment with 100 participants assigned to one of four repair strategies (apologies, denials, explanations, or promises) over three trust violations. Individual attitudes did moderate the efficacy of repair strategies and this effect differed over successive trust violations. Specifically, repair strategies were most effective relative to individual attitude during the second of the three trust violations, and promises were the trust repair strategy most impacted by an individual’s attitude.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171268/1/Esterwood and Roboert 2022 HRI.pdfDescription of Esterwood and Roboert 2022 HRI.pdf : PreprintSEL

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

We typically think of artificial intelligence (AI) as focusing on empowering machines with human capabilities so that they can function on their own, but, in truth, much of AI focuses on intelligence augmentation (IA), which is to augment human capabilities. We propose a framework for designing intelligent augmentation (IA) systems and it addresses six central questions about IA: why, what, who/whom, how, when, and where. To address the how aspect, we introduce four guiding principles: simplification, interpretability, human-centeredness, and ethics. The what aspect includes an IA architecture that goes beyond the direct interactions between humans and machines by introducing their indirect relationships through data and domain. The architecture also points to the directions for operationalizing the IA design simplification principle. We further identify some potential risks and emerging issues in IA design and development to suggest new questions for future IA research and to foster its positive impact on humanity

Are human-like robots trusted like humans? An investigation into the effect of anthropomorphism on trust in robots measured by expected value as reflected by feedback related negativity and P300

Robots are becoming more prevalently used in industry and society. However, in order to ensure effective use of the trust, must be calibrated correctly. Anthropomorphism is one factors which is important in trust in robots (Hancock et al., 2011). Questionnaires and investment games have been used to investigate the impact of anthropomorphism on trust, however, these methods have led to disparate findings. Neurophysiological methods have also been used as an implicit measure of trust. Feedback related negativity (FRN) and P300 are event related potential (ERP) components which have been associated with processes involved in trust such as outcome evaluation. This study uses the trust game (Berg et al., 1995), along with questionnaires and ERP data to investigate trust and expectations towards three agents varying in anthropomorphism, a human, an anthropomorphic robot, and a computer. The behavioural and self-reported findings suggest that the human is perceived as the most trustworthy and there is no difference between the robot and the computer. The ERP data revealed a robot driven difference in FRN and P300 activation, which suggests that robots violated expectations more so than a human or a computer. The present findings are explained in terms of the perfect automation schema and trustworthiness and dominance perceptions. Future research into the impact of voice pitch on dominance and trustworthiness and the impact of trust violations is suggested in order to gain a more holistic picture of the impact of anthropomorphism on trust

A Broad View on Robot Self-Defense: Rapid Scoping Review and Cultural Comparison

With power comes responsibility: as robots become more advanced and prevalent, the role they will play in human society becomes increasingly important. Given that violence is an important problem, the question emerges if robots could defend people, even if doing so might cause harm to someone. The current study explores the broad context of how people perceive the acceptability of such robot self-defense (RSD) in terms of (1) theory, via a rapid scoping review, and (2) public opinion in two countries. As a result, we summarize and discuss: increasing usage of robots capable of wielding force by law enforcement and military, negativity toward robots, ethics and legal questions (including differences to the well-known trolley problem), control in the presence of potential failures, and practical capabilities that such robots might require. Furthermore, a survey was conducted, indicating that participants accepted the idea of RSD, with some cultural differences. We believe that, while substantial obstacles will need to be overcome to realize RSD, society stands to gain from exploring its possibilities over the longer term, toward supporting human well-being in difficult times