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» Load estimation and control using learned dynamics models
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ICCV
2001
IEEE
14 years 9 months ago
Topology Free Hidden Markov Models: Application to Background Modeling
Hidden Markov Models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Th...
Bjoern Stenger, Visvanathan Ramesh, Nikos Paragios...
CGI
2001
IEEE
13 years 11 months ago
An Inverse Kinematics Method Based on Muscle Dynamics
Inverse kinmatics is one of the most popular method in computer graphics to control 3D multi-joint characters. In this paper, we propose an inverse kinematics algorithm that takes...
Taku Komura, Yoshihisa Shinagawa, Tosiyasu L. Kuni...
IROS
2009
IEEE
145views Robotics» more  IROS 2009»
14 years 2 months ago
Automated manipulation of spherical objects in three dimensions using a gimbaled air jet
— This paper presents a mechanism and a control strategy that enables automated non-contact manipulation of spherical objects in three dimensions using air flow, and demonstrate...
Aaron Becker, Robert Sandheinrich, Timothy Bretl
TNN
1998
100views more  TNN 1998»
13 years 7 months ago
A dynamical system perspective of structural learning with forgetting
—Structural learning with forgetting is an established method of using Laplace regularization to generate skeletal artificial neural networks. In this paper we develop a continu...
D. A. Miller, J. M. Zurada
IROS
2008
IEEE
211views Robotics» more  IROS 2008»
14 years 2 months ago
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
Jonathan Ko, Dieter Fox