Estimating the non-linear dynamics of free-flying objects

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@Article{Kim20121108,
  author =       "Seungsu Kim and Aude Billard",
  title =        "Estimating the non-linear dynamics of free-flying
                 objects",
  journal =      "Robotics and Autonomous Systems",
  volume =       "60",
  number =       "9",
  pages =        "1108--1122",
  year =         "2012",
  ISSN =         "0921-8890",
  DOI =          "doi:10.1016/j.robot.2012.05.022",
  URL =          "http://www.sciencedirect.com/science/article/pii/S092188901200084X",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Dynamical systems",
  abstract =     "This paper develops a model-free method to estimate
                 the dynamics of free-flying objects. We take a
                 realistic perspective to the problem and investigate
                 tracking accurately and very rapidly the trajectory and
                 orientation of an object so as to catch it in flight.
                 We consider the dynamics of complex objects where the
                 grasping point is not located at the centre of mass.

                 To achieve this, a density estimate of the
                 translational and rotational velocity is built based on
                 the trajectories of various examples. We contrast the
                 performance of six non-linear regression methods
                 (Support Vector Regression (SVR) with Radial Basis
                 Function (RBF) kernel, SVR with polynomial kernel,
                 Gaussian Mixture Regression (GMR), Echo State Network
                 (ESN), Genetic Programming (GP) and Locally Weighted
                 Projection Regression (LWPR)) in terms of precision of
                 recall, computational cost and sensitivity to choice of
                 hyper-parameters. We validate the approach for
                 real-time motion tracking of 5 daily life objects with
                 complex dynamics (a ball, a fully-filled bottle, a
                 half-filled bottle, a hammer and a pingpong racket). To
                 enable real-time tracking, the estimated model of the
                 object's dynamics is coupled with an Extended Kalman
                 Filter for robustness against noisy sensing.",
  notes =        "GPTIPS",
}

Genetic Programming entries for Seungsu Kim Aude Billard

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