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  • CONFERENCE PAPER
    Jamali N, Kormushev P, Vinas AC, Carreras M, Caldwell DGet al., 2015,

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

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 3915-3920, ISSN: 1050-4729
  • CONFERENCE PAPER
    Jamisola RS, Kormushev P, Caldwell DG, Ibikunle Fet al., 2015,

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

    , Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) And Robotics, Automation and Mechatronics (RAM), Publisher: IEEE
  • BOOK CHAPTER
    Kormushev P, Ahmadzadeh SR, 2015,

    Robot learning for persistent autonomy

    , Studies in Systems, Decision and Control, Pages: 3-28

    © Springer International Publishing Switzerland 2015. Autonomous robots are not very good at being autonomous. They work well in structured environments, but fail quickly in the real world facing uncertainty and dynamically changing conditions. In this chapter, we describe robot learning approaches that help to elevate robot autonomy to the next level, the so-called ‘persistent autonomy’. For a robot to be ‘persistently autonomous’ means to be able to perform missions over extended time periods (e.g. days or months) in dynamic, uncertain environments without need for human assistance. In particular, persistent autonomy is extremely important for robots in difficult-to-reach environments such as underwater, rescue, and space robotics. There are many facets of persistent autonomy, such as: coping with uncertainty, reacting to changing conditions, disturbance rejection, fault tolerance, energy efficiency and so on. This chapter presents a collection of robot learning approaches that addressmany of these facets. Experimentswith robot manipulators and autonomous underwater vehicles demonstrate the usefulness of these learning approaches in real world scenarios.

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

    Encoderless Position Control of a Two-Link Robot Manipulator

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 943-949, ISSN: 1050-4729
  • CONFERENCE PAPER
    Kormushev P, Demiris Y, Caldwell DG, 2015,

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

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 5518-5525, ISSN: 2153-0858
  • CONFERENCE PAPER
    Kryczka P, Kormushev P, Tsagarakis NG, Caldwell DGet al., 2015,

    Online Regeneration of Bipedal Walking Gait Pattern Optimizing Footstep Placement and Timing

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3352-3357, ISSN: 2153-0858
  • 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

    , Pages: 238-243

    © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. PANDORA is a EU FP7 project that is developing new computational methods to make underwater robots Persistently Autonomous, significantly reducing the frequency of assistance requests. The aim of the project is to extend the range of tasks that can be carried on autonomously and increase their complexity while reducing the need for operator assistances. Dynamic adaptation to the change of conditions is very important while addressing autonomy in the real world and not just in well-known situation. The key of Pandora is the ability to recognise failure and respond to it, at all levels of abstraction. Under the guidance of major industrial players, validation tasks of inspection, cleaning and valve turning have been trialled with partners' AUVs in Scotland and Spain.

  • JOURNAL ARTICLE
    Takano W, Asfour T, Kormushev P, 2015,

    Special Issue on Humanoid Robotics PREFACE

    , ADVANCED ROBOTICS, Vol: 29, Pages: 301-301, ISSN: 0169-1864
  • CONFERENCE PAPER
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2014,

    Multi-objective reinforcement learning for AUV thruster failure recovery

    © 2014 IEEE. This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles (AUV). The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the vehicle. When a fault is detected and isolated the model of the AUV is reconfigured according to the new condition. To discover a set of optimal solutions a multi-objective reinforcement learning approach is employed which can deal with multiple conflicting objectives. Each optimal solution can be used to generate a trajectory that is able to navigate the AUV towards a specified target while satisfying multiple objectives. The discovered policies are executed on the robot in a closed-loop using AUV's state feedback. Unlike most existing methods which disregard the faulty thruster, our approach can also deal with partially broken thrusters to increase the persistent autonomy of the AUV. In addition, the proposed approach is applicable when the AUV either becomes under-actuated or remains redundant in the presence of a fault. We validate the proposed approach on the model of the Girona500 AUV.

  • CONFERENCE PAPER
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2014,

    Multi-Objective Reinforcement Learning for AUV Thruster Failure Recovery

    , IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), Publisher: IEEE, Pages: 103-110
  • CONFERENCE PAPER
    Ahmadzadeh SR, Kormushev P, Jamisola RS, Caldwell DGet al., 2014,

    Learning Reactive Robot Behavior for Autonomous Valve Turning

    , 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Publisher: IEEE, Pages: 366-373, ISSN: 2164-0572
  • CONFERENCE PAPER
    Ahmadzadeh SR, Leonetti M, Carrera A, Carreras M, Kormushev P, Caldwell DGet al., 2014,

    Online Discovery of AUV Control Policies to Overcome Thruster Failures

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 6522-6528, ISSN: 1050-4729
  • 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
    Carrera A, Palomeras N, Hurtós N, Kormushev P, Carreras Met al., 2014,

    Learning by demonstration applied to underwater intervention

    , Pages: 95-104, ISSN: 0922-6389

    Performing subsea intervention tasks is a challenge due to the complexities of the underwater domain. We propose to use a learning by demonstraition algorithm to intuitively teach an intervention autonomous underwater vehicle (I-AUV) how to perform a given task. Taking as an input few operator demonstrations, the algorithm generalizes the task into a model and simultaneously controls the vehicle and the manipulator (using 8 degrees of freedom) to reproduce the task. A complete framework has been implemented in order to integrate the LbD algorithm with the different onboard sensors and actuators. A valve turning intervention task is used to validate the full framework through real experiments conducted in a water tank.

  • 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

    © 2014 IEEE. Intervention autonomous underwater vehicles (I-AUVs) are a promising platform to perform intervention task in underwater environments, replacing current methods like remotely operate underwater vehicles (ROVs) and manned sub-mersibles that are more expensive. This article proposes a complete system including all the necessary elements to perform a valve turning task using an I-AUV. The knowledge of an operator to perform the task is transmitted to an I-AUV by a learning by demonstration (LbD) algorithm. The algorithm learns the trajectory of the vehicle and the end-effector to accomplish the valve turning. The method has shown its feasibility in a controlled environment repeating the learned task with different valves and configurations.

  • CONFERENCE PAPER
    Dallali H, Kormushev P, Tsagarakis NG, Caldwell DGet al., 2014,

    Can Active Impedance Protect Robots from Landing Impact?

    , 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Publisher: IEEE, Pages: 1022-1027, ISSN: 2164-0572
  • 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
    Jamali N, Kormushev P, Ahmadzadeh SR, Caldwell DGet al., 2014,

    Covariance analysis as a measure of policy robustness

    © 2014 IEEE. In this paper we propose covariance analysis as a metric for reinforcement learning to improve the robustness of a learned policy. The local optima found during the exploration are analyzed in terms of the total cumulative reward and the local behavior of the system in the neighborhood of the optima. The analysis is performed in the solution space to select a policy that exhibits robustness in uncertain and noisy environments. We demonstrate the utility of the method using our previously developed system where an autonomous underwater vehicle (AUV) has to recover from a thruster failure [1]. When a failure is detected the recovery system is invoked, which uses simulations to learn a new controller that utilizes the remaining functioning thrusters to achieve the goal of the AUV, that is, to reach a target position. In this paper, we use covariance analysis to examine the performance of the top, n, policies output by the previous algorithm. We propose a scoring metric that uses the output of the covariance analysis, the time it takes the AUV to reach the target position and the distance between the target position and the AUV's final position. The top polices are simulated in a noisy environment and evaluated using the proposed scoring metric to analyze the effect of noise on their performance. The policy that exhibits more tolerance to noise is selected. We show experimental results where covariance analysis successfully selects a more robust policy that was ranked lower by the original algorithm.

  • CONFERENCE PAPER
    Jamali N, Kormushev P, Caldwell DG, 2014,

    Robot-Object Contact Perception using Symbolic Temporal Pattern Learning

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 6542-6548, ISSN: 1050-4729
  • CONFERENCE PAPER
    Jamisola RS, Kormushev P, Bicchi A, Caldwell DGet al., 2014,

    Haptic Exploration of Unknown Surfaces with Discontinuities

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 1255-1260, ISSN: 2153-0858

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