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  • Conference paper
    Maurelli F, Lane D, Kormushev P, Caldwell D, Carreras M, Salvi J, Fox M, Long D, Kyriakopoulos K, Karras Get al., 2016,

    The PANDORA project: a success story in AUV autonomy

    , OCEANS Conference 2016, Publisher: IEEE, ISSN: 0197-7385

    This paper presents some of the results of the EU-funded project PANDORA - Persistent Autonomy Through Learning Adaptation Observation and Re-planning. The project was three and a half years long and involved several organisations across Europe. The application domain is underwater inspection and intervention, a topic particularly interesting for the oil and gas sector, whose representatives constituted the Industrial Advisory Board. Field trials were performed at The Underwater Centre, in Loch Linnhe, Scotland, and in harbour conditions close to Girona, Spain.

  • Journal article
    Russo AM, Ma J, Lobo J, Le Fet al., 2016,

    Declarative Framework for Specification, Simulation and Analysis of Distributed Applications

    , IEEE Transactions on Knowledge and Data Engineering, Vol: 28, Pages: 1489-1502, ISSN: 1558-2191

    Researchers have recently shown that declarative database query languages, such as Datalog, could naturally be used to specify and implement network protocols and services. In this paper we present a declarative framework for the specification, execution, simulation and analysis of distributed applications. Distributed applications, including routing protocols, can be specified using a Declarative Networking language, called D2C, whose semantics captures the notion of a Distributed State Machine (DSM), i.e. a network of computational nodes that communicate with each other through the exchange of data. The D2C specification can be directly executed using the DSM computational infrastructure of our framework. The same specification can be simulated and formally verified. The simulation component integrates the DSM tool within a network simulation environment and allows developers to simulate network dynamics and collect data about the execution in order to evaluate application responses to network changes. The formal analysis component of our framework, instead, complements the empirical testing by supporting the verification of different classes of properties of distributed algorithms, including convergence of network routing protocols. To demonstrate the generality of our framework, we show how it can be used to analyse two classes of network routing protocols, a path vector and a Mobile Ad-Hoc Network (MANET) routing protocol, and execute a distributed algorithm for pattern formation in multi-robot systems.

  • Conference paper
    Pesl P, Herrero P, Reddy M, Oliver N, Toumazou C, Georgiou Pet al., 2016,

    Live Demonstration: Smartwatch Implementation of an Advanced Insulin Bolus Calculator for Diabetes

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2370-2370, ISSN: 0271-4302
  • Conference paper
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2016,

    Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units.

    , Pages: 154-170
  • Conference paper
    Maestre C, Cully AHR, Gonzales C, Doncieux Set al., 2015,

    Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach

    , 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Publisher: IEEE

    Determining in advance all objects that a robot will interact with in an open environment is very challenging, if not impossible. It makes difficult the development of models that will allow to perceive and recognize objects, to interact with them and to predict how these objects will react to interactions with other objects or with the robot. Developmental robotics proposes to make robots learn by themselves such models through a dedicated exploration step. It raises a chicken-and-egg problem: the robot needs to learn about objects to discover how to interact with them and, to this end, it needs to interact with them. In this work, we propose Novelty-driven Evolutionary Babbling (NovEB), an approach enabling to bootstrap this process and to acquire knowledge about objects in the surrounding environment without requiring to include a priori knowledge about the environment, including objects, or about the means to interact with them. Our approach consists in using an evolutionary algorithm driven by a novelty criterion defined in the raw sensorimotor flow: behaviours, described by a trajectory of the robot end effector, are generated with the goal to maximize the novelty of raw perceptions. The approach is tested on a simulated PR2 robot and is compared to a random motor babbling.

  • Conference paper
    Athakravi D, Satoh K, Law M, Broda K, Russo AMet al., 2015,

    Automated inference of rules with exception from past legal cases using ASP

    , International Conference on Logic Programming and Non Monotonic Reasoning (LPNMR 2015), Publisher: Springer, Pages: 83-96, ISSN: 0302-9743

    In legal reasoning, different assumptions are often considered when reaching a final verdict and judgement outcomes strictly depend on these assumptions. In this paper, we propose an approach for generating a declarative model of judgements from past legal cases, that expresses a legal reasoning structure in terms of principle rules and exceptions. Using a logic-based reasoning technique, we are able to identify from given past cases different underlying defaults (legal assumptions) and compute judgements that (i) cover all possible cases (including past cases) within a given set of relevant factors, and (ii) can make deterministic predictions on final verdicts for unseen cases. The extracted declarative model of judgements can then be used to make automated inference of future judgements, and generate explanations of legal decisions.

  • Journal article
    Law M, Russo A, Broda K, 2015,

    Learning weak constraints in answer set programming

    , Theory and Practice of Logic Programming, Vol: 15, Pages: 511-525, ISSN: 1475-3081

    This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.

  • Conference paper
    Kormushev P, Demiris Y, Caldwell DG, 2015,

    Kinematic-free Position Control of a 2-DOF Planar Robot Arm

  • Conference paper
    Kryczka P, Kormushev P, Tsagarakis N, Caldwell DGet al., 2015,

    Online Regeneration of Bipedal Walking Gait Optimizing Footstep Placement and Timing

  • Journal article
    Carrera A, Palomeras N, Hurtós N, Kormushev P, Carreras Met al., 2015,

    Cognitive System for Autonomous Underwater Intervention

    , Pattern Recognition Letters, ISSN: 0167-8655
  • Conference paper
    Kormushev P, Demiris Y, Caldwell DG, 2015,

    Encoderless Position Control of a Two-Link Robot Manipulator

  • Conference paper
    Jamali N, Kormushev P, Carrera A, Carreras M, Caldwell DGet al., 2015,

    Underwater Robot-Object Contact Perception using Machine Learning on Force/Torque Sensor Feedback

  • Conference paper
    Carrera A, Palomeras N, Hurtos N, Kormushev P, Carreras Met al., 2015,

    Learning multiple strategies to perform a valve turning with underwater currents using an I-AUV

  • Conference paper
    Ahmadzadeh SR, Paikan A, Mastrogiovanni F, Natale L, Kormushev P, Caldwell DGet al., 2015,

    Learning Symbolic Representations of Actions from Human Demonstrations

  • Journal article
    Cully A, Clune J, Tarapore D, Mouret J-Bet al., 2015,

    Robots that can adapt like animals

    , Nature, Vol: 521, Pages: 503-507, ISSN: 0028-0836

    As robots leave the controlled environments of factories to autonomouslyfunction in more complex, natural environments, they will have to respond tothe inevitable fact that they will become damaged. However, while animals canquickly adapt to a wide variety of injuries, current robots cannot "thinkoutside the box" to find a compensatory behavior when damaged: they are limitedto their pre-specified self-sensing abilities, can diagnose only anticipatedfailure modes, and require a pre-programmed contingency plan for every type ofpotential damage, an impracticality for complex robots. Here we introduce anintelligent trial and error algorithm that allows robots to adapt to damage inless than two minutes, without requiring self-diagnosis or pre-specifiedcontingency plans. Before deployment, a robot exploits a novel algorithm tocreate a detailed map of the space of high-performing behaviors: This maprepresents the robot's intuitions about what behaviors it can perform and theirvalue. If the robot is damaged, it uses these intuitions to guide atrial-and-error learning algorithm that conducts intelligent experiments torapidly discover a compensatory behavior that works in spite of the damage.Experiments reveal successful adaptations for a legged robot injured in fivedifferent ways, including damaged, broken, and missing legs, and for a roboticarm with joints broken in 14 different ways. This new technique will enablemore robust, effective, autonomous robots, and suggests principles that animalsmay use to adapt to injury.

  • Conference paper
    Lane DM, Maurelli F, Kormushev P, Carreras M, Fox M, Kyriakopoulos Ket al., 2015,

    PANDORA - Persistent Autonomy through Learning, Adaptation, Observation and Replanning

  • Conference paper
    Jamisola RS, Kormushev P, Caldwell DG, Ibikunle Fet al., 2015,

    Modular Relative Jacobian for Dual-Arms and the Wrench Transformation Matrix

  • Conference paper
    Athakravi D, Alrajeh D, Broda K, Russo A, Satoh Ket al., 2015,

    Inductive Learning Using Constraint-Driven Bias

    , 24th International Conference on Inductive Logic Programming (ILP), Publisher: SPRINGER-VERLAG BERLIN, Pages: 16-32, ISSN: 0302-9743
  • Journal article
    Bimbo J, Kormushev P, Althoefer K, Liu Het al., 2015,

    Global Estimation of an Object’s Pose Using Tactile Sensing

    , Advanced Robotics, Vol: 29
  • Journal article
    Takano W, Asfour T, Kormushev P, 2015,

    Special Issue on Humanoid Robotics

    , Advanced Robotics, Vol: 29

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