Micro-timing of backchannels in human-robot interaction

Inden B, Malisz Z, Wagner P, Wachsmuth I. Micro-timing of backchannels in human-robot interaction. Presented at the Timing in Human-Robot Interaction: Workshop in Conjunction with the 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI2014), Bielefeld, Germany

The impact of peoples' personal dispositions and personalities on their trust of robots in an emergency scenario

Humans should be able to trust that they can safely interact with their home companion robot. However, robots can exhibit occasional mechanical, programming or functional errors. We hypothesise that the severity of the consequences and the timing of a robot's different types of erroneous behaviours during an interaction may have different impacts on users' attitudes towards a domestic robot. First, we investigated human users' perceptions of the severity of various categories of potential errors that are likely to be exhibited by a domestic robot. Second, we used an interactive storyboard to evaluate participants' degree of trust in the robot after it performed tasks either correctly, or with 'small' or 'big' errors. Finally, we analysed the correlation between participants' responses regarding their personality, predisposition to trust other humans, their perceptions of robots, and their interaction with the robot. We conclude that there is correlation between the magnitude of an error performed by a robot and the corresponding loss of trust by the human towards the robot. Moreover we observed that some traits of participants' personalities (conscientiousness and agreeableness) and their disposition of trusting other humans (benevolence) significantly increased their tendency to trust a robot more during an emergency scenario.Peer reviewe

Developing a timed navigation architecture for hospital delivery robots

In hospitals, typical tasks of delivering goods between different locations are usually done by auxiliary staff. With the development of robotic technologies, such tasks can be performed by mobile robots releasing the staff effort to other tasks. In order to successfully complete the tasks of delivering goods inside hospitals, mobile robots should be able to generate trajectories free of collisions. In addition, including timing constraints to the generated trajectories has not been addressed in most current robotic systems, and it is critical in robotic tasks as human-robot interaction. Including timing constraints means to obey to the planned movement time, despite diversified environmental conditions or perturbations. In this paper we aim to develop a navigation architecture with timing constraints based on a mesh of nonlinear dynamical systems and feedthrough maps for wheeled mobile robots. A simulated hospital environment and a wheeled robot pioneer 3-DX are used to demonstrate the robustness and reliability of the proposed architecture in cluttered, dynamic and uncontrolled hospital scenarios

Evaluating people's perceptions of trust in a robot in a repeated interactions study

Funding Information: Acknowledgment. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642667 (Safety Enables Cooperation in Uncertain Robotic Environments - SECURE). KD acknowledges funding from the Canada 150 Research Chairs Program. Publisher Copyright: © 2020, Springer Nature Switzerland AG This is a post-peer-review, pre-copyedit version of an article published of 'Rossi A., Dautenhahn K., Koay K.L., Walters M.L., Holthaus P. (2020) Evaluating People’s Perceptions of Trust in a Robot in a Repeated Interactions Study. In: Wagner A.R. et al. (eds) Social Robotics. ICSR 2020. Lecture Notes in Computer Science, vol 12483. Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_38'Trust has been established to be a key factor in fostering human-robot interactions. However, trust can change overtime according to different factors, including a breach of trust due to a robot’s error. In this exploratory study, we observed people’s interactions with a companion robot in a real house, adapted for human-robot interaction experimentation, over three weeks. The interactions happened in six scenarios in which a robot performed different tasks under two different conditions. Each condition included fourteen tasks performed by the robot, either correctly, or with errors with severe consequences on the first or last day of interaction. At the end of each experimental condition, participants were presented with an emergency scenario to evaluate their trust in the robot. We evaluated participants’ trust in the robot by observing their decision to trust the robot during the emergency scenario, and by collecting their views through questionnaires. We concluded that there is a correlation between the timing of an error with severe consequences performed by the robot and the corresponding loss of trust of the human in the robot. In particular, people’s trust is subjected to the initial mental formation

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

Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot

In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.Peer reviewedFinal Published versio

Enactivism and Robotic Language Acquisition: A Report from the Frontier

In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.Peer reviewedFinal Published versio