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  • 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
  • Conference paper
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2014,

    Multi-Objective Reinforcement Learning for AUV Thruster Failure Recovery

  • Conference paper
    Dallali H, Kormushev P, Tsagarakis N, Caldwell DGet al., 2014,

    Can Active Impedance Protect Robots from Landing Impact?

  • Conference paper
    Ahmadzadeh SR, Jamisola RS, Kormushev P, Caldwell DGet al., 2014,

    Learning Reactive Robot Behavior for Autonomous Valve Turning

  • Journal article
    Kadir SN, Goodman DFM, Harris KD, 2014,

    High-Dimensional Cluster Analysis with the Masked EM Algorithm

    , Neural Computation, Vol: 26, Pages: 2379-2394, ISSN: 0899-7667

    Cluster analysis faces two problems in high dimensions: the "curse of dimensionality" that can lead to overfitting and poor generalization performance and the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of spike sorting for nextgeneration, high-channel-count neural probes. In this problem, only a small subset of features provides information about the cluster membership of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective.We introduce a "masked EM" algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data and to real-world high-channel-count spike sorting data.

  • Conference paper
    Jamisola RS, Kormushev P, Bicchi A, Caldwell DGet al., 2014,

    Haptic Exploration of Unknown Surfaces with Discontinuities

  • Conference paper
    Ahmadzadeh SR, Carrera A, Leonetti M, Kormushev P, Caldwell DGet al., 2014,

    Online Discovery of AUV Control Policies to Overcome Thruster Failures

  • Conference paper
    Jamali N, Kormushev P, Caldwell DG, 2014,

    Robot-Object Contact Perception using Symbolic Temporal Pattern Learning

  • Conference paper
    Carrera A, Karras G, Bechlioulis C, Palomeras N, Hurtos N, Kyriakopoulos K, Kormushev P, Carreras Met al., 2014,

    Improving a Learning by Demonstration framework for Intervention AUVs by means of an UVMS controller

  • Conference paper
    Jamali N, Kormushev P, Ahmadzadeh SR, Caldwell DGet al., 2014,

    Covariance Analysis as a Measure of Policy Robustness in Reinforcement Learning

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