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  • Conference paper
    Kormushev P, Caldwell DG, 2012,

    Direct policy search reinforcement learning based on particle filtering

  • Journal article
    Colasanto L, Kormushev P, Tsagarakis N, Caldwell DGet al., 2012,

    Optimization of a compact model for the compliant humanoid robot COMAN using reinforcement learning

    , International Journal of Cybernetics and Information Technologies, Vol: 12, Pages: 76-85, ISSN: 1311-9702

    COMAN is a compliant humanoid robot. The introduction of passive compliance in some of its joints affects the dynamics of the whole system. Unlike traditional stiff robots, there is a deflection of the joint angle with respect to the desired one whenever an external torque is applied. Following a bottom up approach, the dynamic equations of the joints are defined first. Then, a new model which combines the inverted pendulum approach with a three-dimensional (Cartesian) compliant model at the level of the center of mass is proposed. This compact model is based on some assumptions that reduce the complexity but at the same time affect the precision. To address this problem, additional parameters are inserted in the model equation and an optimization procedure is performed using reinforcement learning. The optimized model is experimentally validated on the COMAN robot using several ZMP-based walking gaits.

  • Conference paper
    Kormushev P, Caldwell DG, 2012,

    Simultaneous discovery of multiple alternative optimal policies by reinforcement learning

    , Pages: 202-207
  • Journal article
    Shen H, Yosinski J, Kormushev P, Caldwell DG, Lipson Het al., 2012,

    Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization

    , 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
    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
  • 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
  • Conference paper
    Dickens L, Molly I, Lobo J, Chen P, Russo Aet al., 2012,

    Learning Stochastic Models of Information Flow

    , 28th IEEE International Conference on Data Engineering (ICDE), Publisher: IEEE Computer Society, Pages: 570-581, ISSN: 1063-6382
  • 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
    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, 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
    Goodman DFM, Brette R, 2010,

    Learning to localise sounds with spiking neural networks

    To localise the source of a sound, we use location-specific properties of the signals received at the two ears caused by the asymmetric filtering of the original sound by our head and pinnae, the head-related transfer functions (HRTFs). These HRTFs change throughout an organism's lifetime, during development for example, and so the required neural circuitry cannot be entirely hardwired. Since HRTFs are not directly accessible from perceptual experience, they can only be inferred from filtered sounds. We present a spiking neural network model of sound localisation based on extracting location-specific synchrony patterns, and a simple supervised algorithm to learn the mapping between synchrony patterns and locations from a set of example sounds, with no previous knowledge of HRTFs. After learning, our model was able to accurately localise new sounds in both azimuth and elevation, including the difficult task of distinguishing sounds coming from the front and back.

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