13,788 research outputs found

    On the evolution of behaviors through embodied imitation

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    © 2015 Massachusetts Institute of Technology. Abstract This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated

    The direct perception hypothesis: perceiving the intention of another’s action hinders its precise imitation

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    We argue that imitation is a learning response to unintelligible actions, especially to social conventions. Various strands of evidence are converging on this conclusion, but further progress has been hampered by an outdated theory of perceptual experience. Comparative psychology continues to be premised on the doctrine that humans and nonhuman primates only perceive others’ physical ‘surface behavior’, while mental states are perceptually inaccessible. However, a growing consensus in social cognition research accepts the Direct Perception Hypothesis: primarily we see what others aim to do; we do not infer it from their motions. Indeed, physical details are overlooked – unless the action is unintelligible. On this basis we hypothesize that apes’ propensity to copy the goal of an action, rather than its precise means, is largely dependent on its perceived intelligibility. Conversely, children copy means more often than adults and apes because, uniquely, much adult human behavior is completely unintelligible to unenculturated observers due to the pervasiveness of arbitrary social conventions, as exemplified by customs, rituals, and languages. We expect the propensity to imitate to be inversely correlated with the familiarity of cultural practices, as indexed by age and/or socio-cultural competence. The Direct Perception Hypothesis thereby helps to parsimoniously explain the most important findings of imitation research, including children’s over-imitation and other species-typical and age-related variations

    Dynamic mapping strategies for interactive art installations: an embodied combined HCI HRI HHI approach

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    This paper proposes a theoretical framework for dealing with the paradigm of interactivity in new media art, and how the broad use of the term in different research fields can lead to some misunderstandings. The paper addresses a conceptual view on how we can implement interaction in new media art from an embodied approach that unites views from HCI, HRI and HHI. The focus is on an intuitive mapping of a multitude of sensor data and to extend upon this using the paradigm of (1) finite state machines (FSM) to address dynamic mapping strategies, (2) mediality to address aisthesis and (3) embodiment to address valid mapping strategies originated from natural body movements. The theory put forward is illustrated by a case study

    The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning

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    In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed

    Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

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    “This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

    Sketching sonic interactions by imitation-driven sound synthesis

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    Sketching is at the core of every design activity. In visual design, pencil and paper are the preferred tools to produce sketches for their simplicity and immediacy. Analogue tools for sonic sketching do not exist yet, although voice and gesture are embodied abilities commonly exploited to communicate sound concepts. The EU project SkAT-VG aims to support vocal sketching with computeraided technologies that can be easily accessed, understood and controlled through vocal and gestural imitations. This imitation-driven sound synthesis approach is meant to overcome the ephemerality and timbral limitations of human voice and gesture, allowing to produce more refined sonic sketches and to think about sound in a more designerly way. This paper presents two main outcomes of the project: The Sound Design Toolkit, a palette of basic sound synthesis models grounded on ecological perception and physical description of sound-producing phenomena, and SkAT-Studio, a visual framework based on sound design workflows organized in stages of input, analysis, mapping, synthesis, and output. The integration of these two software packages provides an environment in which sound designers can go from concepts, through exploration and mocking-up, to prototyping in sonic interaction design, taking advantage of all the possibilities of- fered by vocal and gestural imitations in every step of the process

    Introduction: The Fourth International Workshop on Epigenetic Robotics

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    As in the previous editions, this workshop is trying to be a forum for multi-disciplinary research ranging from developmental psychology to neural sciences (in its widest sense) and robotics including computational studies. This is a two-fold aim of, on the one hand, understanding the brain through engineering embodied systems and, on the other hand, building artificial epigenetic systems. Epigenetic contains in its meaning the idea that we are interested in studying development through interaction with the environment. This idea entails the embodiment of the system, the situatedness in the environment, and of course a prolonged period of postnatal development when this interaction can actually take place. This is still a relatively new endeavor although the seeds of the developmental robotics community were already in the air since the nineties (Berthouze and Kuniyoshi, 1998; Metta et al., 1999; Brooks et al., 1999; Breazeal, 2000; Kozima and Zlatev, 2000). A few had the intuition – see Lungarella et al. (2003) for a comprehensive review – that, intelligence could not be possibly engineered simply by copying systems that are “ready made” but rather that the development of the system fills a major role. This integration of disciplines raises the important issue of learning on the multiple scales of developmental time, that is, how to build systems that eventually can learn in any environment rather than program them for a specific environment. On the other hand, the hope is that robotics might become a new tool for brain science similarly to what simulation and modeling have become for the study of the motor system. Our community is still pretty much evolving and “under construction” and for this reason, we tried to encourage submissions from the psychology community. Additionally, we invited four neuroscientists and no roboticists for the keynote lectures. We received a record number of submissions (more than 50), and given the overall size and duration of the workshop together with our desire to maintain a single-track format, we had to be more selective than ever in the review process (a 20% acceptance rate on full papers). This is, if not an index of quality, at least an index of the interest that gravitates around this still new discipline

    Computational and Robotic Models of Early Language Development: A Review

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    We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

    Adorno on Mimetic Rationality: Three Puzzles

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    In this paper, I examine Adorno’s controversial claim that human rationality is inherently mimetic. To do so, I break this claim down into three puzzles (the natural historical puzzle, the metaphysical puzzle, and the epistemic puzzle) and consider each in turn. The first puzzle originates in Adorno’s assertion that in the course of human history the mimetic moment of human thought “is melted together with the rational moment”. So whereas, on his narrative, mimesis has become an intrinsic component of human rationality, it appears that we are oblivious to this state of affair and unable to recognize the workings of mimesis in what we otherwise refer to as rationality. The second puzzle concerns the traditional metaphysical question regarding the possibility of knowledge. Adorno holds that the key to this question lies in the “mimetic moment of knowledge”, which he characterizes as the “moment of the elective affinity between the knower and the known.” The third puzzle concerns his views on how the mimetic moment of thought plays out in our epistemic practices. As he puts it, “consciousness knows of its other as much as it resembles that other,” which seems to entail that our very efforts to conceptualize objects somehow rely on imitative processes. I work out what I take to be the basics of Adorno’s understanding of mimesis and use them to make sense of each puzzle. I argue that Adorno’s insistence on the mimetic component of human rationality isn’t meant to promote more mimetic modes of comportment, but a reflexive awareness of the extent to which our rational activities already rely on imitative (or immersive) processes, even those we view as embodying the strongest claims to the contrary
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