A method for the construction of open approximate models from vector time series Preface PhD research is a largely open proces in which a stimulating environment plays a crucial role. I am indebted to several people and institutions that shaped such an environment for me during the years since April 1990. First and for all, I would like to thank my supervisor, Christiaan Heij. He set me on the ...

We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible sequential nature) of training data arising in reinforcement learning we employ a subspace based variant of LS-SVM that sequentially processes the data and is hence especially suited for online learning. This approach is adapte...

Grasping a curved object free in the plane may be done through rolling a pair of fingers on the object’s boundary. Each finger is equipped with a tactile sensor able to record any instantaneous point contact with the object. Contact kinematics reveal a relationship between the amount of finger rotations and the total curvatures of the boundary segments of the fingers and the object respectively...

The dynamics of a PHANTOM R © Premium 1.5A haptic device from SensAble Technologies, Inc. is experimentally identified and analyzed for different installations of the device and its accessories, such as the typical upright, upside-down, with gimbal and counter-balance weight, and with force sensor . An earlier formulation of the robot dynamic model is augmented with a friction model, linearly p...

The inverse kinematics problem of a reclaimer which excavates and transports raw materials in a raw yard is investigated. Because of the geometric feature of the equipment of which scooping buckets are attached around a rotating disk, kinematic redundancy occurs in aktermining joint variables. Link coordinates are introduced following the Denavit-Hatenberg representation. For a given excavation...

Ivan Selesnick March 7, 2013 NYU-Poly These notes address (approximate) solutions to linear equations by least squares. We deal with the ‘easy’ case wherein the system matrix is full rank. If the system matrix is rank deficient, then other methods are needed, e.g., QR decomposition, singular value decomposition, or the pseudo-inverse, [2, 3]. In these notes, least squares is illustrated by appl...