Below is a list of all relevant publications authored by Robotics Forum members.

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  • 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

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

    Encoderless Position Control of a Two-Link Robot Manipulator

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

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

  • Journal article
    Takano W, Asfour T, Kormushev P, 2015,

    Special Issue on Humanoid Robotics

    , Advanced Robotics, Vol: 29
  • 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
  • Conference paper
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2014,

    Multi-Objective Reinforcement Learning for AUV Thruster Failure Recovery

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

    Learning Reactive Robot Behavior for Autonomous Valve Turning

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

    Can Active Impedance Protect Robots from Landing Impact?

  • 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

  • Conference paper
    Carrera A, Palomeras N, Ribas D, Kormushev P, Carreras Met al., 2014,

    An Intervention-AUV learns how to perform an underwater valve turning

  • Conference paper
    Carrera A, Palomeras N, Hurtos N, Kormushev P, Carreras Met al., 2014,

    Learning by demonstration applied to underwater intervention

  • Conference paper
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2013,

    Autonomous robotic valve turning: A hierarchical learning approach

    , 2013 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 4629-4634, ISSN: 1050-4729

    Autonomous valve turning is an extremely challenging task for an Autonomous Underwater Vehicle (AUV). To resolve this challenge, this paper proposes a set of different computational techniques integrated in a three-layer hierarchical scheme. Each layer realizes specific subtasks to improve the persistent autonomy of the system. In the first layer, the robot acquires the motor skills of approaching and grasping the valve by kinesthetic teaching. A Reactive Fuzzy Decision Maker (RFDM) is devised in the second layer which reacts to the relative movement between the valve and the AUV, and alters the robot's movement accordingly. Apprenticeship learning method, implemented in the third layer, performs tuning of the RFDM based on expert knowledge. Although the long-term goal is to perform the valve turning task on a real AUV, as a first step the proposed approach is tested in a laboratory environment. © 2013 IEEE.

  • Conference paper
    Karras GC, Bechlioulis CP, Leonetti M, Palomeras N, Kormushev P, Kyriakopoulos KJ, Caldwell DGet al., 2013,

    On-Line Identification of Autonomous Underwater Vehicles through Global Derivative-Free Optimization

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