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  • 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, 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
  • 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
    Kormushev P, Calinon S, Ugurlu B, Caldwell DGet al., 2012,

    Challenges for the policy representation when applying reinforcement learning in robotics

    , Pages: 1-8
  • 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
  • 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
    Lane DM, Maurelli F, Kormushev P, Carreras M, Fox M, Kyriakopoulos Ket al., 2012,

    Persistent Autonomy: the Challenges of the PANDORA Project

  • 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
    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, 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
  • 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
  • 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
    Wong MZ, Guillard B, Murai R, Saeedi S, Kelly PHJet al.,

    AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors

    We present a high-speed, energy-efficient Convolutional Neural Network (CNN)architecture utilising the capabilities of a unique class of devices known asanalog Focal Plane Sensor Processors (FPSP), in which the sensor and theprocessor are embedded together on the same silicon chip. Unlike traditionalvision systems, where the sensor array sends collected data to a separateprocessor for processing, FPSPs allow data to be processed on the imagingdevice itself. This unique architecture enables ultra-fast image processing andhigh energy efficiency, at the expense of limited processing resources andapproximate computations. In this work, we show how to convert standard CNNs toFPSP code, and demonstrate a method of training networks to increase theirrobustness to analog computation errors. Our proposed architecture, coinedAnalogNet, reaches a testing accuracy of 96.9% on the MNIST handwritten digitsrecognition task, at a speed of 2260 FPS, for a cost of 0.7 mJ per frame.

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