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  • Conference paper
    Leonetti M, Ahmadzadeh SR, Kormushev P, 2013,

    On-line Learning to Recover from Thruster Failures on Autonomous Underwater Vehicles

  • Conference paper
    Kormushev P, Caldwell DG, 2013,

    Towards Improved AUV Control Through Learning of Periodic Signals

  • Book
    Deisenroth MP, Neumann G, Peters J, 2013,

    A Survey on Policy Search for Robotics

    , Publisher: now Publishers

    Policy search is a subfield in reinforcement learning which focuses onfinding good parameters for a given policy parametrization. It is wellsuited for robotics as it can cope with high-dimensional state and actionspaces, one of the main challenges in robot learning. We review recentsuccesses of both model-free and model-based policy search in robotlearning.Model-free policy search is a general approach to learn policiesbased on sampled trajectories. We classify model-free methods based ontheir policy evaluation strategy, policy update strategy, and explorationstrategy and present a unified view on existing algorithms. Learning apolicy is often easier than learning an accurate forward model, and,hence, model-free methods are more frequently used in practice. How-ever, for each sampled trajectory, it is necessary to interact with the robot, which can be time consuming and challenging in practice. Model-based policy search addresses this problem by first learning a simulatorof the robot’s dynamics from data. Subsequently, the simulator gen-erates trajectories that are used for policy learning. For both model-free and model-based policy search methods, we review their respectiveproperties and their applicability to robotic systems.

  • Conference paper
    Kormushev P, Caldwell DG, 2013,

    Reinforcement Learning with Heterogeneous Policy Representations

  • Conference paper
    Kryczka P, Hashimoto K, Takanishi A, Kormushev P, Tsagarakis N, Caldwell DGet al., 2013,

    Walking Despite the Passive Compliance: Techniques for Using Conventional Pattern Generators to Control Instrinsically Compliant Humanoid Robots

  • Conference paper
    Carrera A, Carreras M, Kormushev P, Palomeras N, Nagappa Set al., 2013,

    Towards valve turning with an AUV using Learning by Demonstration

  • Conference paper
    Kryczka P, Kormushev P, Hashimoto K, Lim H-O, Tsagarakis NG, Caldwell DG, Takanishi Aet al., 2013,

    Hybrid gait pattern generator capable of rapid and dynamically consistent pattern regeneration

    , Publisher: IEEE, Pages: 475-480
  • Journal article
    Filippi S, Barnes CP, Cornebise J, Stumpf MPHet al., 2013,

    On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo

  • Conference paper
    Kryczka P, Shiguematsu YM, Kormushev P, Hashimoto K, Lim H-O, Takanishi Aet al., 2013,

    Towards dynamically consistent real-time gait pattern generation for full-size humanoid robots

  • Journal article
    Kormushev P, Calinon S, Caldwell DG, 2013,

    Reinforcement Learning in Robotics: Applications and Real-World Challenges

    , Robotics, Vol: 2, Pages: 122-148, ISSN: 2218-6581

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