Towards hybrid primary intersubjectivity: a neural robotics library for human science

Human-robot interaction is becoming an interesting area of research in cognitive science, notably, for the study of social cognition. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We argue this sort of low level cognitive interaction, where control is shared in dyadic encounters, is susceptible of study with neural robots. Hence, in this work we pursue three main objectives. Firstly, from the concept of active inference we study primary intersubjectivity as a second person perspective experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. Secondly, we propose an open-source methodology named \textit{neural robotics library} (NRL) for experimental human-robot interaction, and a demonstration program for interacting in real-time with a virtual Cartesian robot (VCBot). Lastly, through a study case, we discuss some ways human-robot (hybrid) intersubjectivity can contribute to human science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation

Towards a Cognitive Architecture for Socially Adaptive Human-Robot Interaction

People have a natural predisposition to interact in an adaptive manner with others, by instinctively changing their actions, tones and speech according to the perceived needs of their peers. Moreover, we are not only capable of registering the affective and cognitive state of our partners, but over a prolonged period of interaction we also learn which behaviours are the most appropriate and well-suited for each one of them individually. This universal trait that we share regardless of our different personalities is referred to as social adaptation (adaptability). Humans are always capable of adapting to the others although our personalities may influence the speed and efficacy of the adaptation. This means that in our everyday lives we are accustomed to partake in complex and personalized interactions with our peers. Carrying this ability to personalize to human-robot interaction (HRI) is highly desirable since it would provide user-personalized interaction, a crucial element in many HRI scenarios - interactions with older adults, assistive or rehabilitative robotics, child-robot interaction (CRI), and many others. For a social robot to be able to recreate this same kind of rich, human-like interaction, it should be aware of our needs and affective states and be capable of continuously adapting its behaviour to them. Equipping a robot with these functionalities however is not a straightforward task. A robust approach for solving this is implementing a framework for the robot supporting social awareness and adaptation. In other words, the robot needs to be equipped with the basic cognitive functionalities, which would allow the robot to learn how to select the behaviours that would maximize the pleasantness of the interaction for its peers, while being guided by an internal motivation system that would provide autonomy to its decision-making process. The goal of this research was threefold: attempt to design a cognitive architecture supporting social HRI and implement it on a robotic platform; study how an adaptive framework of this kind would function when tested in HRI studies with users; and explore how including the element of adaptability and personalization in a cognitive framework would in reality affect the users - would it bring an additional richness to the human-robot interaction as hypothesized, or would it instead only add uncertainty and unpredictability that would not be accepted by the robot`s human peers? This thesis covers the work done on developing a cognitive framework for human-robot interaction; analyzes the various challenges of implementing the cognitive functionalities, porting the framework on several robotic platforms and testing potential validation scenarios; and finally presents the user studies performed with the robotic platforms of iCub and MiRo, focused on understanding how a cognitive framework behaves in a free-form HRI context and if humans can be aware and appreciate the adaptivity of the robot. In summary, this thesis had the task of approaching the complex field of cognitive HRI and attempt to shed some light on how cognition and adaptation develop from both the human and the robot side in an HRI scenario

A model for long-term human-robot interaction and relationships in a companion robot

Humans have different cognitive thoughts, own personality, traits, cognitive characteristics and certain anxiety which have large impact on their thoughts. Humans are not perfect, but cognitive characteristics make humans what they are, and when humans interact, their cognitive personality reflects on their characteristics behaviours. Humans like/ dislike each other depending on their cognitive characteristics and then relationship forms. But, in human-robot interactions, the robot usually lacks of human-like cognitive personality, mood & emotions and cognitive bias effect in its behavioural characteristics. The current research is solely inspired by human’s cognitive characteristics and interaction processes, and it aims to develop above human-like factors in autonomous robotic system and test it with human users to study how that will affect the human-robot interactions and long-term relationships

Interaction and Experience in Enactive Intelligence and Humanoid Robotics

We overview how sensorimotor experience can be operationalized for interaction scenarios in which humanoid robots acquire skills and linguistic behaviours via enacting a “form-of-life”’ in interaction games (following Wittgenstein) with humans. The enactive paradigm is introduced which provides a powerful framework for the construction of complex adaptive systems, based on interaction, habit, and experience. Enactive cognitive architectures (following insights of Varela, Thompson and Rosch) that we have developed support social learning and robot ontogeny by harnessing information-theoretic methods and raw uninterpreted sensorimotor experience to scaffold the acquisition of behaviours. The success criterion here is validation by the robot engaging in ongoing human-robot interaction with naive participants who, over the course of iterated interactions, shape the robot’s behavioural and linguistic development. Engagement in such interaction exhibiting aspects of purposeful, habitual recurring structure evidences the developed capability of the humanoid to enact language and interaction games as a successful participant

Comparison of Human Social Brain Activity During Eye-Contact With Another Human and a Humanoid Robot

Robot design to simulate interpersonal social interaction is an active area of research with applications in therapy and companionship. Neural responses to eye-to-eye contact in humans have recently been employed to determine the neural systems that are active during social interactions. Whether eye-contact with a social robot engages the same neural system remains to be seen. Here, we employ a similar approach to compare human-human and human-robot social interactions. We assume that if human-human and human-robot eye-contact elicit similar neural activity in the human, then the perceptual and cognitive processing is also the same for human and robot. That is, the robot is processed similar to the human. However, if neural effects are different, then perceptual and cognitive processing is assumed to be different. In this study neural activity was compared for human-to-human and human-to-robot conditions using near infrared spectroscopy for neural imaging, and a robot (Maki) with eyes that blink and move right and left. Eye-contact was confirmed by eye-tracking for both conditions. Increased neural activity was observed in human social systems including the right temporal parietal junction and the dorsolateral prefrontal cortex during human-human eye contact but not human-robot eye-contact. This suggests that the type of human-robot eye-contact used here is not sufficient to engage the right temporoparietal junction in the human. This study establishes a foundation for future research into human-robot eye-contact to determine how elements of robot design and behavior impact human social processing within this type of interaction and may offer a method for capturing difficult to quantify components of human-robot interaction, such as social engagement