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Journal articleKormushev P, Calinon S, Caldwell DG, 2013,
Conference paperKryczka P, Kormushev P, Hashimoto K, et al., 2013,
Hybrid gait pattern generator capable of rapid and dynamically consistent pattern regeneration, Publisher: IEEE, Pages: 475-480
Conference paperDallali H, Mosadeghzad M, Medrano-Cerda GA, et al., 2013,
Development of a dynamic simulator for a compliant humanoid robot based on a symbolic multibody approach, Pages: 598-603
Conference paperKryczka P, Shiguematsu YM, Kormushev P, et al., 2013,
Towards dynamically consistent real-time gait pattern generation for full-size humanoid robots
Journal articleBarnes CP, Filippi S, Stumpf MPH, et al., 2012,
Journal articleDeisenroth MP, Turner RD, Huber MF, et al., 2012,
Journal articleBarnes C, Filippi S, Stumpf MPH, et 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 paperKormushev P, Caldwell DG, 2012,
Direct policy search reinforcement learning based on particle filtering
Journal articleColasanto L, Kormushev P, Tsagarakis N, et al., 2012,
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 articleShen H, Yosinski J, Kormushev P, et al., 2012,
Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization, International Journal of Cybernetics and Information Technologies, Vol: 12
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