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  • Conference paper
    Dallali H, Mosadeghzad M, Medrano-Cerda GA, Docquier N, Kormushev P, Tsagarakis N, Li Z, Caldwell Det al., 2013,

    Development of a dynamic simulator for a compliant humanoid robot based on a symbolic multibody approach

    , Pages: 598-603
  • Journal article
    Silk D, Filippi S, Stumpf MPH, 2013,

    Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems

  • Journal article
    Barnes CP, Filippi S, Stumpf MPH, Thorne Tet al., 2012,

    Considerate approaches to constructing summary statistics for ABC model selection

    , STATISTICS AND COMPUTING, Vol: 22, Pages: 1181-1197, ISSN: 0960-3174
  • Journal article
    Deisenroth MP, Turner RD, Huber MF, Hanebeck UD, Rasmussen CEet al., 2012,

    Robust Filtering and Smoothing with Gaussian Processes

    , IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 57, Pages: 1865-1871, ISSN: 0018-9286
  • Journal article
    Barnes C, Filippi S, Stumpf MPH, Thorne Tet al., 2012,

    Considerate approaches to achieving sufficiency for ABC model selection

    , Statistics and Computing, Vol: 22, Pages: 1181-1197, ISSN: 0960-3174

    For nearly any challenging scientific problemevaluation of the likelihood is problematic if not impossible.Approximate Bayesian computation (ABC) allowsus to employ the whole Bayesian formalism to problemswhere we can use simulations from a model, but cannotevaluate the likelihood directly. When summary statistics ofreal and simulated data are compared—rather than the datadirectly—information is lost, unless the summary statisticsare sufficient. Sufficient statistics are, however, not commonbut without them statistical inference in ABC inferencesare to be considered with caution. Previously other authorshave attempted to combine different statistics in order toconstruct (approximately) sufficient statistics using searchand information heuristics. Here we employ an informationtheoreticalframework that can be used to construct appropriate(approximately sufficient) statistics by combining differentstatistics until the loss of information is minimized.We start from a potentially large number of different statisticsand choose the smallest set that captures (nearly) thesame information as the complete set. We then demonstratethat such sets of statistics can be constructed for both parameterestimation and model selection problems, and we applyour approach to a range of illustrative and real-world modelselection problems.

  • 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.

  • 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
    Lane DM, Maurelli F, Kormushev P, Carreras M, Fox M, Kyriakopoulos Ket al., 2012,

    Persistent Autonomy: the Challenges of the PANDORA Project

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