A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 regression models that can predict user ratings (e.g., humanlike) from the design features of the hands (e.g., number of fingers). The models have less than a 10-point error in predicting the user ratings on a 0-100 scale. The shape of the fingertips, color scheme, and size of the hands influence the user ratings the most. We present simple guidelines to improve user impression of robot hands and outline remaining questions for future work.
Technical University of Darmstadt（达姆施塔特工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
One of the first and foremost non-verbal interactions that humans perform is a handshake. It has an impact on first impressions as touch can convey complex emotions. This makes handshaking an important skill for the repertoire of a social robot. In this paper, we present a novel framework for learning reaching behaviours for human-robot handshaking behaviours for humanoid robots solely using third-person human-human interaction data. This is especially useful for non-backdrivable robots that cannot be taught by demonstrations via kinesthetic teaching. Our approach can be easily executed on dif…
Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation
Department of Robotics, University of Michigan（机器人系，密歇根大学）；LAAS-CNRS, University of Toulouse（LAAS-CNRS，图卢兹大学）；INRIA, University of Lorraine（INRIA，洛林大学）
Social robots are increasingly introduced into children's lives as educational and social companions, yet little is known about how these products might best be introduced to their environments. The emergence of the "unboxing" phenomenon in media suggests that introduction is key to technology adoption where initial impressions are made. To better understand this phenomenon toward designing a positive unboxing experience in the context of social robots for children, we conducted three field studies with families of children aged 8 to 13: (1) an exploratory free-play activity ($n=12$); (2) a co…
Emotional Expression in Low-Degrees-of-Freedom Robots: Assessing Perception with Reachy Mini
Robotic Musicianship Lab（机器人音乐性实验室）；Georgia Institute of Technology（佐治亚理工学院）；School of Computing and Communications（计算与通讯学院）；Ben-Gurion University of the Negev（贝内尔盖翁大学（内盖夫分校））；School of Brain Sciences and Cognition（脑科学与认知学院）；The Azrieli National Center for Autism and Neurodevelopment Research（阿兹里尔国家自闭症与神经发育研究中心）
To provide effective and enjoyable human-robot interaction, it is important for social robots to exhibit nonverbal behaviors, such as a handshake or a hug. However, the traditional approach of reproducing pre-coded motions allows users to easily predict the reaction of the robot, giving the impression that the robot is a machine rather than a real agent. Therefore, we propose a neural network architecture based on the Seq2Seq model that learns social behaviors from human-human interactions in an end-to-end manner. We adopted a generative adversarial network to prevent invalid pose sequences fr…
Unconscious and Intentional Human Motion Cues for Expressive Robot-Arm Motion Design
School of Engineering University of Hyogo（工程学院 京都大学）；Faculty of Informatics Kansai University（信息学院 关西大学）；Graduate School of Engineering University of Hyogo（工学研究院 京都大学）
Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）；University of Bristol（布里斯托尔大学）；University of Warwick（华威大学）；Emotix Inc（Emotix公司）
HAPI: A Model for Learning Robot Facial Expressions from Human Preferences
Graduate School of Informatics, Kyoto University（京都大学信息学研究生院）；Psychological Process Research Team, Guardian Robot Project, RIKEN（守护机器人项目心理过程研究组，RIKEN）；Field Science Education and Research Center, Kyoto University（京都大学田野科学教育研究中心）；Interactive Robot Research Team, Guardian Robot Project, RIKEN（守护机器人项目交互机器人研究组，RIKEN）；Research and Development Center for Large Language Models, National Institute of Informatics（信息处理研究所大型语言模型研究开发中心）
Japanese character figurines are popular and have pivot position in Otaku culture. Although numerous robots have been developed, less have focused on otaku-culture or on embodying the anime character figurine. Therefore, we take the first steps to bridge this gap by developing Hatsuki, which is a humanoid robot platform with anime based design. Hatsuki's novelty lies in aesthetic design, 2D facial expressions, and anime-style behaviors that allows it to deliver rich interaction experiences resembling anime-characters. We explain our design implementation process of Hatsuki, followed by our eva…
Known-Voice Cloning and the Limits of Personality Transfer for Character Design
Augsburg University（奥格斯堡大学）；Institute of Intelligent Systems and Robotics（智能系统与机器人研究所）；CNRS（法国国家科学研究中心）；Sorbonne Université（索邦大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate tha…
Institute for Creative Technologies, University of Southern California（南加州大学创意技术研究所）；Electronics and Telecommunications Research Institute（韩国电子通信研究院）
Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robots by human users and will find applications in education, training, and medical services. One challenge in learning the co-speech gesture model is that there may be multiple viable gesture motions for the same speech utterance. The deterministic regression methods can not resolve the conflicting samples and may produce over-smoothed or damped motio…
