Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems

As robotic systems are moved out of factory work cells into human-facing environments questions of choreography become central to their design, placement, and application. With a human viewer or counterpart present, a system will automatically be interpreted within context, style of movement, and form factor by human beings as animate elements of their environment. The interpretation by this human counterpart is critical to the success of the system's integration: knobs on the system need to make sense to a human counterpart; an artificial agent should have a way of notifying a human counterpart of a change in system state, possibly through motion profiles; and the motion of a human counterpart may have important contextual clues for task completion. Thus, professional choreographers, dance practitioners, and movement analysts are critical to research in robotics. They have design methods for movement that align with human audience perception, can identify simplified features of movement for human-robot interaction goals, and have detailed knowledge of the capacity of human movement. This article provides approaches employed by one research lab, specific impacts on technical and artistic projects within, and principles that may guide future such work. The background section reports on choreography, somatic perspectives, improvisation, the Laban/Bartenieff Movement System, and robotics. From this context methods including embodied exercises, writing prompts, and community building activities have been developed to facilitate interdisciplinary research. The results of this work is presented as an overview of a smattering of projects in areas like high-level motion planning, software development for rapid prototyping of movement, artistic output, and user studies that help understand how people interpret movement. Finally, guiding principles for other groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for the 21st Century)" http://www.mdpi.com/journal/arts/special_issues/Machine_Artis

Understanding Anthropomorphism in Service Provision: A Meta-Analysis of Physical Robots, Chatbots, and other AI

An increasing number of firms introduce service robots, such as physical robots and virtual chatbots, to provide services to customers. While some firms use robots that resemble human beings by looking and acting humanlike to increase customers’ use intention of this technology, others employ machinelike robots to avoid uncanny valley effects, assuming that very humanlike robots may induce feelings of eeriness. There is no consensus in the service literature regarding whether customers’ anthropomorphism of robots facilitates or constrains their use intention. The present meta-analysis synthesizes data from 11,053 individuals interacting with service robots reported in 108 independent samples. The study synthesizes previous research to clarify this issue and enhance understanding of the construct. We develop a comprehensive model to investigate relationships between anthropomorphism and its antecedents and consequences. Customer traits and predispositions (e.g., computer anxiety), sociodemographics (e.g., gender), and robot design features (e.g., physical, nonphysical) are identified as triggers of anthropomorphism. Robot characteristics (e.g., intelligence) and functional characteristics (e.g., usefulness) are identified as important mediators, although relational characteristics (e.g., rapport) receive less support as mediators. The findings clarify contextual circumstances in which anthropomorphism impacts customer intention to use a robot. The moderator analysis indicates that the impact depends on robot type (i.e., robot gender) and service type (i.e., possession-processing service, mental stimulus-processing service). Based on these findings, we develop a comprehensive agenda for future research on service robots in marketing

Bridging the gap between emotion and joint action

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd. Joint action brings individuals (and embodiment of their emotions) together, in space and in time. Yet little is known about how individual emotions propagate through embodied presence in a group, and how joint action changes individual emotion. In fact, the multi-agent component is largely missing from neuroscience-based approaches to emotion, and reversely joint action research has not found a way yet to include emotion as one of the key parameters to model socio-motor interaction. In this review, we first identify the gap and then stockpile evidence showing strong entanglement between emotion and acting together from various branches of sciences. We propose an integrative approach to bridge the gap, highlight five research avenues to do so in behavioral neuroscience and digital sciences, and address some of the key challenges in the area faced by modern societies

Robotic Psychology. What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn?

“Robotization”, the integration of robots in human life will change human life drastically. In many situations, such as in the service sector, robots will become an integrative part of our lives. Thus, it is vital to learn from extant research on human-robot interaction (HRI). This article introduces robotic psychology that aims to bridge the gap between humans and robots by providing insights into particularities of HRI. It presents a conceptualization of robotic psychology and provides an overview of research on service-focused human-robot interaction. Theoretical concepts, relevant to understand HRI with are reviewed. Major achievements, shortcomings, and propositions for future research will be discussed

From automata to animate beings: the scope and limits of attributing socialness to artificial agents

Understanding the mechanisms and consequences of attributing socialness to artificial agents has important implications for how we can use technology to lead more productive and fulfilling lives. Here, we integrate recent findings on the factors that shape behavioral and brain mechanisms that support social interactions between humans and artificial agents. We review how visual features of an agent, as well as knowledge factors within the human observer, shape attributions across dimensions of socialness. We explore how anthropomorphism and dehumanization further influence how we perceive and interact with artificial agents. Based on these findings, we argue that the cognitive reconstruction within the human observer is likely to be far more crucial in shaping our interactions with artificial agents than previously thought, while the artificial agent's visual features are possibly of lesser importance. We combine these findings to provide an integrative theoretical account based on the “like me” hypothesis, and discuss the key role played by the Theory‐of‐Mind network, especially the temporal parietal junction, in the shift from mechanistic to social attributions. We conclude by highlighting outstanding questions on the impact of long‐term interactions with artificial agents on the behavioral and brain mechanisms of attributing socialness to these agents

I, chatbot! the impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentions

The present research focuses on the interplay between two common features of the customer service chatbot experience: gaze direction and anthropomorphism. Although the dominant approach in marketing theory and practice is to make chatbots as human-like as possible, the current study, built on the humanness-value-loyalty model, addresses the chain of effects through which chatbots' nonverbal behaviors affect customers' willingness to disclose personal information and purchase intentions. By means of two experiments that adopt a real chatbot in a simulated shopping environment (i.e., car rental and travel insurance), the present work allows us to understand how to reduce individuals' tendency to see conversational agents as less knowledgeable and empathetic compared with humans. The results show that warmth perceptions are affected by gaze direction, whereas competence perceptions are affected by anthropomorphism. Warmth and competence perceptions are found to be key drivers of consumers' skepticism toward the chatbot, which, in turn, affects consumers' trust toward the service provider hosting the chatbot, ultimately leading consumers to be more willing to disclose their personal information and to repatronize the e-tailer in the future. Building on the Theory of Mind, our results show that perceiving competence from a chatbot makes individuals less skeptical as long as they feel they are good at detecting others' ultimate intentions

Human Path Prediction using Auto Encoder LSTMs and Single Temporal Encoders

Due to automation, the world is changing at a rapid pace. Autonomous agents have become more common over the last several years and, as a result, have created a need for improved software to back them up. The most important aspect of this greater software is path prediction, as robots need to be able to decide where to move in the future. In order to accomplish this, a robot must know how to avoid humans, putting frame prediction at the core of many modern day solutions. A popular way to solve this complex problem of frame prediction is Auto Encoder LSTMs. Though there are many implementations of this, at its core, it is a neural network comprised of a series of time sensitive processing blocks that shrink and then grow the data’s dimensions to make a prediction. The idea of using Auto Encoder styled networks to do frame prediction has also been adapted by others to make Temporal Encoders. These neural networks work much like traditional Auto Encoders, in which the data is reduced then expanded back up. These networks attempt to tease out a series of frames, including a predictive frame of the future. The problem with many of these networks is that they take an immense amount of computation power, and time to get them performing at an acceptable level. This thesis presents possible ways of pre-processing input frames to these networks in order to gain performance, in the best case seeing a 360x improvement in accuracy compared to the original models. This thesis also extends the work done with Temporal Encoders to create more precise prediction models, which showed consistent improvements of at least 50% for some metrics. All of the generated models were compared using a simulated data set collected from recordings of ground level viewpoints from Cities: Skylines. These predicted frames were then analyzed using a common perceptual distance metric, that is, Minkowski distance, as well as a custom metric that tracked distinct areas in frames. All of the following was run on a constrained system in order to see the effects of the changes as they pertain to systems with limited hardware access

Social top-down response modulation (STORM): a model of the control of mimicry in social interaction

Human social interaction is complex and dynamic(Hari and Kujala,2009).Individuals communicate with each other by means of multiple verbal and nonverbal behaviors,which rapidly change from moment to moment.Unraveling mechanisms underlying the subtlety of social behaviors is important for our understanding of the nature of human social interaction. One remarkable nonverbal behavior during social interactions is spontaneous mimicry(van Baarenetal.,2009). People have a tendency to unconsciously imitate other’s behaviors(Chartr and and van Baaren,2009). In the past decade, this spontaneous mimicry has become the key focus of research in socialpsychology and cognitive neuroscience(Heyes,2009),and has been regarded as a paradigm for exploring the complexity of human social interaction.Investigations of the causes,consequences and brain basis of mimicry have been widely carried out(Chartr and and van Baaren,2009). For example,socialpsychology suggests that mimicry has positive consequences on social interaction;it increases liking and affiliation between interaction partners and makes communication more smooth and enjoyable(Chartr and and Bargh,1999). Cognitive neuroscience further suggests that mimicry is based on the mirror neuron system(MNS)(Catmur et al., 2008, 2009; Heyes, 2011a). This system provides a direct link between perception and action where observing an action automatically activates the motor representation of that action(Brass and Heyes,2005)and this link is most likely developed by associative sequence learning(“the ASL theory,” Heyes,2001,2011a; Catmur et al.,2007,2008,2009). However,two key questions still remain unclear. First,what is the purpose of mimicry? Although the ASL theory clearly elucidates how we develop the ability to mimic,it does not directly explain under what circumstances we will mimic and why we mimic to different degrees in different situations. Second,what brain mechanisms control and implement mimicry responses? In this article we aim to address these two questions by reviewing cutting-edge research on the control of mimicry by social signals. In the first part,we give a brief outline of past theories on the purpose of mimicry and emphasize that mimicry is a strategy for social advantage.We provide evidence that mimicry changes depending on the social context[i.e.,social top-down response modulation (STORM)], and suggest that this subtle control may reflect a Machiavellian strategy for enhancing one’s social standing. In the second part, we move to a neuroscience point of view and examine the information processing systems underlying the control of mimicry. We suggest that medial pre-frontal cortex(mPFC)plays a key role in the control of mimicry in social contexts. Finally, we discuss the importance of the STORM model of mimicry in our understanding of social interaction and social cognition. We argue that subtly controlling when and who to mimic is essential to human competence in social interactions and suggest that impairment of this function could lead to social-communication disorders such as autism

Designing Robotic Movement with Personality

As robots are starting to inhabit more intimate social spheres, their functionality and acceptance in a fundamentally social environment greatly depend on them being tolerated by humans. One factor contributing to successfully accomplishing tasks in a collaborative manner is how robots’ actions and motions are interpreted by the people around them. Our broader research seeks to explore this gap aiming to design movement that is expressive, culturally dependent and contextually sensitive. A country that is at the forefront of this, in terms of social robots and their acceptance in society, is Japan. Therefore, as the first phases of this broader research, we present a new process, including a design toolkit, an open brief and a participatory structure. We discuss the resulting robot morphologies and participant feedback from a workshop in Japan, and conclude by discussing limitations and further research in designing robots with expressive movement, contextually sensitive within an HRI-for-all paradigm