26,687 research outputs found

    Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks

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    In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are usually reflected in deviation from expected sensory traces. These deviations in sensory traces can be used to drive the motion adaptation, and for this purpose, a feedback model is required. The feedback model maps the deviations in sensory traces to the motion plan adaptation. In this paper, we develop a general data-driven framework for learning a feedback model from demonstrations. We utilize a variant of a radial basis function network structure --with movement phases as kernel centers-- which can generally be applied to represent any feedback models for movement primitives. To demonstrate the effectiveness of our framework, we test it on the task of scraping on a tilt board. In this task, we are learning a reactive policy in the form of orientation adaptation, based on deviations of tactile sensor traces. As a proof of concept of our method, we provide evaluations on an anthropomorphic robot. A video demonstrating our approach and its results can be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on Robotics and Automation (ICRA) 201

    Chaotic exploration and learning of locomotion behaviours

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    We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

    Measuring autonomy and emergence via Granger causality

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    Concepts of emergence and autonomy are central to artificial life and related cognitive and behavioral sciences. However, quantitative and easy-to-apply measures of these phenomena are mostly lacking. Here, I describe quantitative and practicable measures for both autonomy and emergence, based on the framework of multivariate autoregression and specifically Granger causality. G-autonomy measures the extent to which the knowing the past of a variable helps predict its future, as compared to predictions based on past states of external (environmental) variables. G-emergence measures the extent to which a process is both dependent upon and autonomous from its underlying causal factors. These measures are validated by application to agent-based models of predation (for autonomy) and flocking (for emergence). In the former, evolutionary adaptation enhances autonomy; the latter model illustrates not only emergence but also downward causation. I end with a discussion of relations among autonomy, emergence, and consciousness

    Agents for educational games and simulations

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    This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

    Near-Death Experiences and Immortality from the Perspective of an Informational Modeling of Consciousness

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    The questions concerning “who we are”, “where we go to”, and “where we come from”, preoccupied the humanity from immemorial times. During the last few decades, with the accelerated improvement of the investigation methods and of the advanced successful interventions allowing the life salvation, there have been reported some attempts to correlate the psychic phenomena with the body status by the recuperation, analysis and explanation of the symptoms recorded during the near-death experiences. Such special situations, in which the heart and the brain, the support of mental activities, cease their activity, has become a fundamental tool to investigate the consciousness associated phenomena during the arrest status of the fundamental processes of the life. The fundamental question actually is whether consciousness really continues to exist even if the body has ceased its function by stopping the heartbeat and brain support activities [1,2]. An answer to such a question goes toward even further questions: can there be “life” beyond the death [3]? This exciting question includes also several aspects: what is consciousness and which is its nature [4]? Could consciousness exist as a disembodied entity? To answer these fundamental questions of existence, the collaboration of several disciplines such as neurology, psychology, medicine, biology, pharmacology and also physics, to call only the most important of them, is necessary, the geriatrics finding suitable responses within the multidisciplinary researches to its various questions related to the life prolongation and the improvement of the life quality. Within such a context, it was recently developed an informational model of consciousness, which can offer response to the above questions, based on the last discoveries of the quantum physics and cosmology [4,5]
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