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  • Journal article
    Dallali H, Kormushev P, Li Z, Caldwell DGet al., 2012,

    On Global Optimization of Walking Gaits for the Compliant Humanoid Robot COMAN Using Reinforcement Learning

    , International Journal of Cybernetics and Information Technologies, Vol: 12
  • Conference paper
    Lane DM, Maurelli F, Kormushev P, Carreras M, Fox M, Kyriakopoulos Ket al., 2012,

    Persistent Autonomy: the Challenges of the PANDORA Project

  • Journal article
    Carrera A, Ahmadzadeh SR, Ajoudani A, Kormushev P, Carreras M, Caldwell DGet al., 2012,

    Towards Autonomous Robotic Valve Turning

    , Cybernetics and Information Technologies, Vol: 12, Pages: 17-26
  • Conference paper
    Kormushev P, Calinon S, Ugurlu B, Caldwell DGet al., 2012,

    Challenges for the policy representation when applying reinforcement learning in robotics

    , Pages: 1-8
  • Conference paper
    Kormushev P, Ugurlu B, Colasanto L, Tsagarakis NG, Caldwell DGet al., 2012,

    The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans

    , Pages: 3706-3713
  • Journal article
    Calinon S, Kormushev P, Caldwell DG, 2012,

    Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning

    , Robotics and Autonomous Systems
  • Journal article
    Leonetti M, Kormushev P, Sagratella S, 2012,

    Combining Local and Global Direct Derivative-free Optimization for Reinforcement Learning

    , International Journal of Cybernetics and Information Technologies, Vol: 12
  • Conference paper
    Kormushev P, Ugurlu B, Calinon S, Tsagarakis NG, Caldwell DGet al., 2011,

    Bipedal walking energy minimization by reinforcement learning with evolving policy parameterization

    , 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), Publisher: IEEE
  • Conference paper
    Kormushev P, Ugurlu B, Calinon S, Tsagarakis N, Caldwell DGet al., 2011,

    Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Parameterization

    , Pages: 318-324
  • Journal article
    Kormushev P, Calinon S, Caldwell DG, 2011,

    Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

    , Advanced Robotics, Vol: 25, Pages: 581-603
  • Conference paper
    Kormushev P, Nenchev DN, Calinon S, Caldwell DGet al., 2011,

    Upper-body Kinesthetic Teaching of a Free-standing Humanoid Robot

    , Pages: 3970-3975
  • Journal article
    Kormushev P, Nomoto K, Dong F, Hirota Ket al., 2011,

    Time Hopping Technique for Faster Reinforcement Learning in Simulations

    , International Journal of Cybernetics and Information Technologies, Vol: 11, Pages: 42-59
  • Conference paper
    Kormushev P, Calinon S, Caldwell DG, 2010,

    Approaches for Learning Human-like Motor Skills which Require Variable Stiffness During Execution

  • Conference paper
    Kormushev P, Calinon S, Saegusa R, Metta Get al., 2010,

    Learning the skill of archery by a humanoid robot iCub

    , Pages: 417-423
  • Conference paper
    Kormushev P, Calinon S, Caldwell DG, 2010,

    Robot Motor Skill Coordination with EM-based Reinforcement Learning

    , Pages: 3232-3237
  • Conference paper
    Sato F, Nishii T, Takahashi J, Yoshida Y, Mitsuhashi M, Kormushev P, Kanamiya Yet al., 2010,

    Whiteboard Cleaning Task Realization with HOAP-2

    , Pages: 426-429
  • Journal article
    Kormushev P, Nomoto K, Dong F, Hirota Ket al., 2009,

    Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning

    , Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol: 13, No. 6
  • Conference paper
    Kormushev P, Dong F, Hirota K, 2009,

    Probability redistribution using time hopping for reinforcement learning

  • Journal article
    Kormushev P, Nomoto K, Dong F, Hirota K, Kormushev P, Nomoto K, Dong F, Hirota Ket al., 2008,

    Time manipulation technique for speeding up reinforcement learning in simulations

    , Cybernetics and Information Technologies, Vol: 8, Pages: 12-24, ISSN: 1311-9702

    A technique for speeding up reinforcement learning algorithms by usingtime manipulation is proposed. It is applicable to failure-avoidance controlproblems running in a computer simulation. Turning the time of the simulationbackwards on failure events is shown to speed up the learning by 260% andimprove the state space exploration by 12% on the cart-pole balancing task,compared to the conventional Q-learning and Actor-Critic algorithms.

  • Conference paper
    Yamazaki Y, Dong F, Masuda Y, Uehara Y, Kormushev P, Vu HA, Le PQ, Hirota Ket al., 2007,

    Intent expression using eye robot for mascot robot system

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