In this paper we introduce the core results of the project on software development for social robotics systems. The usability of maintenance and control features is crucial for many kinds of systems, but in the case of social robotics we also have to take into account that (1) the humanoid robot physically interacts with humans, (2) the conversation with children might have different requirements in comparison to the conversation with adults. The results of our work were implement for the humanoid PAL REEM robot, but their core ideas can be applied for other types of humanoid robots. We developed a web-based solution that supports the management of robot-guided tours, provides recommendations for the users as well as allows for a visual analysis of the data on previous tours.
@cite_26 presented the concept of Social Robot Architecture, which integrates the key elements of agenthood and robotics in a coherent and systematic manner. The ethical and social implications of robotics were discussed by in @cite_25 . @cite_17 examined social-psychology concepts to apply them to the human-robot interaction. @cite_7 presented a case study where they analysed the effects of robot features (human-likeness and gender) and user characteristics on the human-robot interaction acceptance and psychological anthropomorphism. @cite_8 analysed the effects of gesture on the perception of psychological anthropomorphism, by conducting a case study using the Honda humanoid robot. @cite_19 conducted a cross-cultural study on generation of culture dependent facial expressions of humanoid robot. @cite_4 discussed the use of observational studies of human-robot social interaction in open human-inhabited environments. Klein and Cook @cite_20 analysed and compared the findings in the UK and Germany on robot-therapy with emotional robots as a treatment approach for people with cognitive impairments.
@cite_26 @cite_4 @cite_7 @cite_8 @cite_19 @cite_25 @cite_20 @cite_17
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Here, we propose that bidirectionality in implicit motor coordination between humanoid robots and humans could enhance the social competence of human–robot interactions. We first detail some questions pertaining to human–robot interactions, introducing the Uncanny Valley hypothesis. After introducing a framework pertinent for the understanding of natural social interactions, motor resonance, we examine two behaviors derived from this framework: motor coordination, investigated in and informative about human–human interaction, and motor interference, which demonstrate the relevance of the motor resonance framework to describe human perception of humanoid robots. These two lines of investigation are then put together to “close the loop” by proposing to implement a key feature of motor coordination, bidirectionality, in robots’ behavior. Finally, we discuss the feasibility of implementing motor coordination between humanoid robots and humans, and the consequences of this implementation in enhancing the social competence of robots interacting with humans. Keywords: interpersonal interaction, motor resonance, motor coordination, motor interference, social robotics, anthropomorphism As robots enter the everyday physical world of people, it is important that they abide by society's unspoken social rules such as respecting people's personal spaces. In this paper, we explore issues related to human personal space around robots, beginning with a review of the existing literature in human-robot interaction regarding the dimensions of people, robots, and contexts that influence human-robot interactions. We then present several research hypotheses which we tested in a controlled experiment (N=30). Using a 2 (robotics experience vs. none: between-participants) × 2 (robot head oriented toward a participant's face vs. legs: within-participants) mixed design experiment, we explored the factors that influence proxemic behavior around robots in several situations: (1) people approaching a robot, (2) people being approached by an autonomously moving robot, and (3) people being approached by a teleoperated robot. We found that personal experience with pets and robots decreases a person's personal space around robots. In addition, when the robot's head is oriented toward the person's face, it increases the minimum comfortable distance for women, but decreases the minimum comfortable distance for men. We also found that the personality trait of agreeableness decreases personal spaces when people approach robots, while the personality trait of neuroticism and having negative attitudes toward robots increase personal spaces when robots approach people. These results have implications for both human-robot interaction theory and design. Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot's mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot's performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior. This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people's perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants' preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot's behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants' dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori's theoretically proposed uncanny valley' diagram. Suggestions for future work are outlined. This thesis puts forward a computational framework that can be used by embodied artificial agents (and in particular autonomous robots) for ontogenetic development. The research investigates methods, endowed with which, an embodied agent can develop control structures for increasingly complex and better adapted behaviour, explicitly and incrementally from its history of interaction with its environment. The temporal horizon of an agent is extended so that past experience can be self-organized into a developing structure that can be used to anticipate the future and act appropriately in environments where state information is incomplete, such as a social environment. A formal definition of sensorimotor experience is given, and Crutchfield’s information metric is used as the basis for comparison of experiences. Information metrics are demonstrated to be able to characterize and identify time-extended behaviour. A definition of a metric space of experiences is followed by the introduction of an architecture that combines this with environmental reinforcement as the basis for a system for robot ontogeny. The architecture is demonstrated and tested in various robotic and simulation experiments. This thesis also introduces the early communication game “Peekaboo” as a tool for the study of human-robot interaction and development. The interaction history architecture is then used by two different robots to develop the capability to engage in the peekaboo game. HRI researchers interested in social robots have made large investments in humanoid robots. There is still sparse evidence that peoples' responses to robots differ from their responses to computer agents, suggesting that agent studies might serve to test HRI hypotheses. To help us understand the difference between people's social interactions with an agent and a robot, we experimentally compared people's responses in a health interview with (a) a computer agent projected either on a computer monitor or life-size on a screen, (b) a remote robot projected life-size on a screen, or (c) a collocated robot in the same room. We found a few behavioral and large attitude differences across these conditions. Participants forgot more and disclosed least with the collocated robot, next with the projected remote robot, and then with the agent. They spent more time with the collocated robot and their attitudes were most positive toward that robot. We discuss tradeoffs for HRI research of using collocated robots, remote robots, and computer agents as proxies of robots. The BERT2 social robot, a platform for the exploration of human-robot interaction, is currently being built at the Bristol Robotics Laboratory. This paper describes work on the robot's face, a hybrid face composed of a plastic faceplate and an LCD display, and our implementation of facial expressions on this versatile platform. We report the implementation of two representations of affect space, each of which map the space of potential emotions to specific facial feature parameters and the results of a series of human-robot interaction experiments to characterize the recognizability of the robot's archetypal facial expressions. The tested subjects' recognition of the implemented facial expressions for happy, surprised, and sad was robust (with nearly 100 recognition). Subjects, however, tended to confuse the expressions for disgusted and afraid with other expressions, with correct recognition rates of 21.1 and 52.6 respectively. Future work involves the addition of more realistic eye movements for stronger recognition of certain responses. These results demonstrate that a hybrid face with affect space facial expression implementations can provide emotive conveyance readily recognized by human beings. Social psychology offers a perspective on the acceptance and adoption of technology that is not often considered in technical circles. In this paper, we discuss several adoption-of-technology models with respect to the acceptance of domestic robots: we examine social-psychology literature and apply it directly to human-robot interaction. We raise key points that we feel will be pivotal to how domestic users respond to robots, and provide a set of guidelines that roboticists and designers of robotic interfaces can use to consider and analyze their designs. Ultimately, understanding how users respond to robots and the reasons behind their responses will enable designers to creating domestic robots that are accepted into homes. This paper presents a machine vision based ap- proach for human operators to select individual and groups of autonomous robots from a swarm of UAVs. The angular distance between the robots and the human is estimated using measures of the detected human face, which aids to determine human and multi-UAV localization and positioning. In turn, this is exploited to effectively and naturally make the human select the spatially situated robots. Spatial gestures for selecting robots are presented by the human operator using tangible input devices (i.e., colored gloves). To select individuals and groups of robot we formulate a vocabulary of two-handed spatial pointing gestures. With the use of a Support Vector Machine (SVM) trained in a cascaded multi-binary-class configuration, the spatial gestures are effectively learned and recognized by a swarm of UAVs. I. INTRODUCTION Without the use of teleoperated and hand-held interaction devices, human operators generally face difficulties in select- ing and commanding individual and groups of robots from a relatively large group of spatially distributed robots (i.e., a swarm). However, due to the widespread availability of cost effective digital cameras onboard UGVs and UAVs, it is increasing the attention towards developing uninstrumented methods (i.e., methods that do not use sophisticated hardware devices from the human side) for human-swarm interaction (HSI). In previous work, we focused on learning efficient features incrementally (online) from multi-viewpoint images of multiple gestures that were acquired by a swarm of ground robots (1). In this paper, we present a cascaded supervised machine learning approach to deal with the machine vision problem of selecting 3D spatially-situated robots from a networked swarm based on the recognition of spatial hand gestures. These are a natural, easy recognizable, and device- less way to enable human operators to easily interact with external artifacts such as robots. Inspired by natural human behavior, we propose an ap- proach that combines face engagement and pointing gestures to interact with a swarm of robots: standing in front of a population of robots, by looking at them and pointing at them with spatial gestures, a human operator can designate individual or groups of robots of determined size. Robots cooperate to combine their independent observations of the human's face and gestures to cooperatively determine which robots were addressed (i.e., selected). While state of the art computer vision techniques pro- vide excellent face detection, human skeleton, and gesture recognition in ideal conditions, there are often occlusions, Communication between humans and robots is a very critical step for the integration of social robots into society. Emotion expression through a robotic face is one of the key points of communication. Despite the most recent efforts, no matter how much expression capabilities improve, facial expression recognition is often hampered by a cultural divide between subjects that participate in surveys. The purpose of this work is to take advantage of the 24 degrees of freedom head of the humanoid social robot KOBIAN-R for making it capable of displaying different versions of the same expressions, using face and neck, in a way that they are easy to understand for Japanese and for Western subjects. We present a system based on relevant studies of human communication and facial anatomy, as well as on the work of illustrators and cartoonists. The expression generator we developed can be adapted to specific cultures. Results confirmed the in-group advantage, showing that the recognition rate of this system is higher when the nationality of the subjects and the cultural characterisation of the shown expressions are coincident. We conclude that this system could be used, in future, on robots that have to interact in a social environment, with people with different cultural background. Abstract With increasingly competent robotic systems desired and required for social human–robot interaction comes the necessity for more complex means of control. Cognitive architectures (specifically the perspective where principles of structure and function are sought to account for multiple cognitive competencies) have only relatively recently been considered for application to this domain. In this paper, we describe one such set of architectural principles – activation dynamics over a developmental distributed associative substrate – and show how this enables an account of a fundamental competence for social cognition: multi-modal behavioural alignment. Data from real human–robot interactions is modelled using a computational system based on this set of principles to demonstrate how this competence can therefore be considered as embedded in wider cognitive processing. It is shown that the proposed system can model the behavioural characteristics of human subjects. While this study is a simulation using real interaction data, the results obtained validate the application of the proposed approach to this issue. This paper discusses the use of observational studies of human-robot social interaction in open human-inhabited environments as a method for improving on the design and evaluating the interactive capabilities of social robots. First, we discuss issues that have surfaced in attempts to evaluate social interactions between humans and robots. Next, we review two observational studies involving robots interacting socially with humans and discuss how the results can be applied to improving robot design. The first is an analysis of a mobile conference-attending robot that performed a search task by augmenting its perception through social interaction with human attendees. The second is an analysis of a stationary robotic receptionist that provides information to visitors and enhances interaction through story-telling. Through these examples, we show how observational studies can be applied to human-robot social interactions in varying contexts and with differing tasks to quantitatively and qualitatively evaluate (and discover unanticipated aspects of) the social interaction. Finally, we discuss design recommendations suggested by insights gained through these analyses In order for humans and robots to interact in an effective and intuitive manner, robots must obtain information about the human affective state in response to the robot's actions. This secondary mode of interactive communication is hypothesized to permit a more natural collaboration, similar to the "body language" interaction between two cooperating humans. This paper describes the implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus. Inputs to the system are physiological signals such as heart rate, perspiration rate, and facial muscle contraction. Affective state was estimated using a two- dimensional valence-arousal representation. A robot manipulator was used to generate motions expected during human-robot interaction, and human subjects were asked to report their response to these motions. The human physiological response was also measured. Robot motions were generated using both a nominal potential field planner and a recently reported safe motion planner that minimizes the potential collision forces along the path. The robot motions were tested with 36 subjects. This data was used to train and validate the HMM model. The results of the HMM affective estimation are also compared to a previously implemented fuzzy inference engine. Art installations involving robotic artifacts provide an opportunity to examine human relationships with robots designed solely for the purpose of sustaining evocative behaviours. In an attempt to determine the behavioural characteristics and personality traits attributed by a human to a robotic artifact, we investigated an audience’s experience of an installation that presented three robotic artifacts moving autonomously in an exhibition space. In order to describe the audience’s experience, we present two studies that revealed the psychological attributions spontaneously produced from observing the robots, and visitors’ physical exploration patterns inside the exhibition. We propose a psychological profile for the artwork, and a tentative organization for the attribution process. Using a cluster analysis performed on visitors’ trajectories inside the installation, we highlight four different exploration and interaction heuristics characterized by patterns of approach or withdrawal, passive observation and exploration. Recognition of intentions is a subconscious cognitive process vital to human communication. This skill enables anticipation and increases the quality of interactions between humans. Within the context of engagement, non-verbal signals are used to communicate the intention of starting the interaction with a partner. In this paper, we investigated methods to detect these signals in order to allow a robot to know when it is about to be addressed. Originality of our approach resides in taking inspiration from social and cognitive sciences to perform our perception task. We investigate meaningful features, i.e. human readable features, and elicit which of these are important for recognizing someone's intention of starting an interaction. Classically, spatial information like the human position and speed, the human-robot distance are used to detect the engagement. Our approach integrates multimodal features gathered using a companion robot equipped with a Kinect. The evaluation on our corpus collected in spontaneous conditions highlights its robustness and validates the use of such a technique in a real environment. Experimental validation shows that multimodal features set gives better precision and recall than using only spatial and speed features. We also demonstrate that 7 selected features are sufficient to provide a good starting engagement detection score. In our last investigation, we show that among our full 99 features set, the space reduction is not a solved task. This result opens new researches perspectives on multimodal engagement detection. Multimodal approach for starting engagement detection using non-explicit cues.Results show that our approach performs better than spatial one in all conditions.MRMR strategy reduces the features space to 7 features without a performance loss.Validation of Schegloff (sociologist) meaningful features for engagement detection.A robot centered labeled corpus of 4 hours in a home-like environment. We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the âfreezing robot problemâ: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place or performs unnecessary maneuvers to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a âmultiple goalâ extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds 488 runs, specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans m2, while a state-of-the-art non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people m2. We also show that our non-cooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds. Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot's current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrated that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which also makes it applicable to fields such as behavioral science or computer graphics. The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, this work attempts to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, it details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Several algorithms, from naive ones using only object category presence to more sophisticated ones using both objects and relationships, are proposed. Both learning and inference use the information encoded in the underlying representation-objects and relative spatial information between them. The approaches are based on learning from exemplars, clustering and the use of Bayesian network classifiers. The approaches are generative. Further, even though they are based on learning from exemplars, they are not ontology specific; i.e. they do not assume the use of any particular ontology. The presented algorithms rely on a robots inherent high-level feature extraction capability (object recognition and structural element extraction) capability to actually form concept models and infer them. Thus, this report presents methods that could enable a robot to to link sensory information to increasingly abstract concepts (spatial constructs). Such a conceptualization and the representation that results thereof would enable robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of this work is-conceptualization and classification for representation and spatial cognition. The design of an affect recognition system for socially perceptive robots relies on representative data: human-robot interaction in naturalistic settings requires an affect recognition system to be trained and validated with contextualised affective expressions, that is, expressions that emerge in the same interaction scenario of the target application. In this paper we propose an initial computational model to automatically analyse human postures and body motion to detect engagement of children playing chess with an iCat robot that acts as a game companion. Our approach is based on vision-based automatic extraction of expressive postural features from videos capturing the behaviour of the children from a lateral view. An initial evaluation, conducted by training several recognition models with contextualised affective postural expressions, suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making our approach suitable for integration into an affect recognition system for a game companion in a real world scenario. The effects of physical embodiment and physical presence were explored through a survey of 33 experimental works comparing how people interacted with physical robots and virtual agents. A qualitative assessment of the direction of quantitative effects demonstrated that robots were more persuasive and perceived more positively when physically present in a user?s environment than when digitally-displayed on a screen either as a video feed of the same robot or as a virtual character analog; robots also led to better user performance when they were collocated as opposed to shown via video on a screen. However, participants did not respond differently to physical robots and virtual agents when both were displayed digitally on a screen - suggesting that physical presence, rather than physical embodiment, characterizes people?s responses to social robots. Implications for understanding psychological response to physical and virtual agents and for methodological design are discussed. Survey identified 33 works exploring user responses to physical robots and virtual agents.Robot agents had greater influence when physically present than telepresent.No differences were found between physical robots displayed on a screen and virtual agents that looked similar.Physical presence, but not physical embodiment alone, resulted in more favorable responses from participants.