Robots with very human-like faces are expected to be able to display human-like facial expressions. However, manually defining the animations of such facial expressions can be intricate and, thus, methods to automatically animate the actuators of android robot heads are promising. An approach to learn a mapping from human faces to android actuator configurations in a self-supervised way is presented here. Compared to previous approaches, convincing results are achieved with relatively few samples of train data. A capable technology to automatically extract facial expressions from vision input is identified as an important requirement for such approaches. The learned mapping is compared to a manual one in a user study and both are rated mostly positive. In effect, combining both is likely to result in even better mimicry capabilities.

The android robot Andrea was set up at a public museum in Germany for six consecutive days to have conversations with visitors, fully autonomously. No specific context was given, so visitors could state their opinions regarding possible use-cases in structured interviews, without any bias. Additionally the 44 interviewees were asked for their general opinions of the robot, their reasons (not) to interact with it and necessary improvements for future use. The android’s voice and wig were changed between different days of operation to give varying cues regarding its gender. This did not have a significant impact on the positive overall perception of the robot. Most visitors want the robot to provide information about exhibits in the future, while opinions on other roles, like a receptionist, were both wanted and explicitly not wanted by different visitors. Speaking more languages (than only English) and faster response times were the improvements most desired. These findings from the interviews are in line with an analysis of the system logs, which revealed, that after chitchat and personal questions, most of the 4436 collected requests asked for information related to the museum and to converse in a different language. The valuable insights gained from these real-world interactions are now used to improve the system to become a useful real-world application.

This study investigates whether personalized voice cloning can improve a robot’s likability compared to a design-congruent voice and a distinctly dissimilar voice. Participants interacted with a gender-ambiguous android robot in three different voice conditions. We compared: (1) a personalized voice clone based on the participant’s voice, (2) a design-congruent voice matching the robot’s appearance, and (3) a dissimilar voice, which differs from both the participant’s and the robot’s features.The cloned and design-congruent voices significantly increased likability compared to the dissimilar voice, while anthropomorphism and familiarity showed no significant differences across conditions. Most participants did not immediately recognize their cloned voice until informed that one of the voices was a clone. However, most of the participants were successful when asked to pick out their cloned voice from those used. We assume that voice personalization through similarity to the user improves likability even before the user is aware of this similarity.Our results show that personalized voice cloning is a simple alternative to other methods for the design of robotic voices. It significantly increases robot likability while requiring minimal user effort.

Achieving optimal balance in games is essential to their success, yet reliant on extensive manual work and playtesting. To facilitate this process, the Procedural Content Generation via Reinforcement Learning (PCGRL) framework has recently been effectively used to improve the balance of existing game levels. This approach, however, only assesses balance heuristically, neglecting actual human perception. For this reason, this work presents a survey to empirically evaluate the created content paired with human playtesting. Participants in four different scenarios are asked about their perception of changes made to the level both before and after balancing, and vice versa. Based on descriptive and statistical analysis, our findings indicate that the PCGRL-based balancing positively influences players’ perceived balance for most scenarios, albeit with differences in aspects of the balancing between scenarios.

This paper investigates the effects of gender neutralization on the perception of anthropomorphism, gender specificity, and novelty for human voices, comparing spoken and hummed voice modalities. We evaluated gender-neutralized and original voice samples in both spoken and hummed formats using an online survey. Our results confirm that gender-neutralizing filters effectively reduce perceived gender specificity in both modalities, supporting their use in creating gender-neutral voices for humanoid robots. Hummed voices were perceived as more anthropomorphic and less novel than spoken voices, suggesting that non-verbal sound modalities can enhance the human likeness of gender-neutral androids while maintaining gender ambiguity. The study contributes to HRI by highlighting the potential of humming to fulfill users’ expectations of interaction with android robots.

We present two approaches to animate an android robot head according to audio speech input, both are adopted from recent machine learning based works in computer graphics animation. More concrete we implemented a viseme-based and a mesh-based approach on our robot. After a subjective comparison we conduct a speech-reading study to evaluate our preferred, the mesh-based, approach. The results show that on average the intelligibility is not increased by the visual cues provided through the robot head in comparison to noisy audio alone. This underlines the importance of carefully designing and controlling the facial co-speech movements of talking android heads.

This study examines the impact of gender neutralization on the anthropomorphism of speech signals and is motivated by the need to find a computer-generated voice for our android robot Andrea. A filter was used to gender-neutralize recordings from natural and synthetic voices, which were then pre-tested for gender neutrality. The results of the main experiment showed that gender-neutral voices were less anthropomorphic than gender-specific voices, with the naturalness of the voice having a greater impact than gender neutralization itself. Synthetic voices were rated less anthropomorphic than natural voices, and the additional effect of gender neutralization was stronger for natural voices. Overall, anthropomorphism ranked highest for natural gender-specific voices, followed by natural gender-neutralized voices, synthetic gender-specific, and, finally, synthetic gender-neutralized voices. Gender of the participants had no significant impact. These findings have implications for the development of humanoid and social as well as android robots.

Laboratories are being increasingly automated. In small laboratories individual processes can be fully automated, but this is usually not economically viable. Nevertheless, individual process steps can be performed by flexible, mobile robots to relieve the laboratory staff. As a contribution to the requirements in a life science laboratory the mobile, dextrous robot Kevin was designed by the Fraunhofer IPA research institute in Stuttgart, Germany. Kevin is a mobile service robot which is able to fulfill non-value adding activities such as transportation of labware. This paper gives an overview of Kevin’s functionalities, its development process, and presents a preliminary study on how its lights and sounds improve user interaction.

The papers in this special issue focus on computational modeling of emotion recognition. Emotions play a pervasive role in personal, social, and professional life. As artificially intelligent systems become pervasive in our lives, it is important that these systems are able to understand emotion in humans and simulate the function of emotion to be effective in their interactions with people. Computational models of emotion contribute towards this goal by, on the one hand, serving as a means to test emotion theories and help understand the function of emotion and, on the other, as the end in itself by simulating appropriate emotion and its downstream consequences – such as expressions of emotion – in computational agents. This special issue presents a critical overview of this cross-disciplinary field, with contributions from some of the leading scholars in cognitive psychology and affective computing, focusing both on theory and practice.

The main characteristics of service robots are their human-centered working environment and their non-expert users. Therefore, service robots need to exhibit human robot interaction that lets users feel comfortable in order for them to accept these robots at home. In this research, we propose a concept of a kitchen assistant robot supporting people in completing stressful cooking tasks. The stressful cooking tasks in this study are also verified to gain the understanding of the applicability of service robots in a wider sense. For the proposed concept a prototype of the kitchen assistant robot is constructed which consists of a robot arm and a control device Myo gesture armband. 18 university students experienced the prototype by completing the stressful tasks and gave feedback by answering questionnaires. This study investigates the usability and users' acceptance of the kitchen assistant robot.

This paper describes the results of a qualitative analysis of questionnaire data collected during a public exhibition of our robotic tele-presence system. In Summer 2013 the mildly humanized robot DARYL could be tried out by the general public during our University's science fair in the city center. People were given the chance to communicate through the robot with their peers and to perceive the world through the "eyes" and "ears" of the robot by means of a head-mounted display with attached headphones. An operator's voice was instantaneously transmitted to the robot's location and his or her head movements were tracked to enable direct, intuitive control of the robot's head movements. Twenty-seven people were interviewed in a structured way about their impressions and opinions after having either operated or interacted with the tele-operated robot. A careful analysis of the acquired data reveals a rather positive evaluation of the tele-presence system and interesting opinions about suitable application areas. These findings may guide designers of robotic tele-presence systems, a research area of increasing popularity.

This paper introduces MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and an emotionally expressive, virtual face presented on a small, servo-controlled display. MARCO was built to investigate the hypothesis that hybrid systems capable of displaying emotions make playing chess more personal and enjoyable. In addition, it is our aim to realize emotional contagion between man and machine in that the agent has the power to influence the human player on an emotional level and vice versa. The hardware components consist of eight Dynamixel servos, an Arduino-based control board, a 5.6 inch display, and a DGT chessboard. The software components run concurrently as separate processes. The main components are the virtual agent framework MARC, the WASABI Affect Simulation architecture, and the TSCP chess engine.

No abstract available.

We present MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and a virtual agent's face displaying emotions. MARCO was built to investigate the hypothesis that hybrid agents capable of displaying emotions make playing chess more personal and enjoyable. In addition, we aim to explore means of achieving emotional contagion between man and machine.

This paper presents a brief summary of the first workshop on Emotion Representation and Modelling in Human-Computer-Interaction-Systems. The ERM4HCI 2013 workshop is held in conjunction with the ICMI 2013 conference. The focus is on theory driven representation and modelling of emotions in the context of Human-Computer-Interaction.

A computational architecture of emotion is presented which grounds a component of an appraisal cognitive model into the homeostatic processes of a humanoid robot. The focus of the present work entails a `grounding' of the arousal component of the Pleasure Arousal Dominance emotion space into the electrical energy processes of an iCub robot. Key variables and performance criteria for robotic energy autonomous behavior in interaction with human are discussed. We show that our bio-inspired affective architecture offers viable basic cycles in exemplar “two-resource problem” testbed scenarios for a real iCub robot. The expression of the robot's emotional state and its role for the efficient interaction in the proposed two-resource problem task is discussed and experimental designs are presented.

Humans use very sophisticated ways of bodily emotion expression combining facial expressions, sound, gestures and full body posture. Like others, we want to apply these aspects of human communication to ease the interaction between robots and users. In doing so we believe there is a need to consider what abstraction of human social communicative behaviors is appropriate for robots. The study reported in this paper is a pilot study to not offer simulated emotion but to offer an abstracted robot version of emotion expressions and an evaluation to what extent users interpret these robot expressions as the intended emotional states. To this end, we present the mobile, mildly humanized robot Daryl, for which we created six motion sequences that combine human-like, animal-like, and robot-specific social cues. The results of a user study (N=29) show that despite the absence of facial expressions and articulated extremities, subjects' interpretation of Daryl's emotional states were congruent with the abstracted emotion display. These results demonstrate that abstract displays of emotion that combine human-like, animal-like, and robot-specific modalities could in fact be an alternative to complex facial expressions and will feed into ongoing work identifying robot-specific social cues.

Since most robots are not easily displayable in real-life scenarios, only a few studies investigate users' behavior towards humanoids or androids in a natural environment. We present an observational field study and data on unscripted interactions between humans and the android robot "Geminoid HI-1". First results show that almost half of the subjects mistook Geminoid HI-1 for a human. Even those who recognized the android as a robot rather showed interest than negative emotions and explored the robots capabilities.

People like to play, and robotic technology offers the opportunity to interact with artifacts in new ways. Robots co-existing with humans in domestic and public environments are expected to behave as companions, also engaging in playful interaction. If a robot is small, we foresee that people will want to be able to pick it up and express their intentions playfully by hugging, shaking and moving it around in various ways. Such robots will need to recognize these gestures--which we call “full-body gestures” because they affect the robot's full body. Inertial sensors inside the robot could be used to detect these gestures, in order to avoid having to rely on external sensors in the environment. However, it is not obvious which gestures typically occur during play, and which of these can be reliably detected. We therefore investigate full-body gesture recognition using Sponge Robot, a small humanoid robot equipped with inertial sensors and designed for playful human-robot interaction.