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» Gaussian Process Dynamical Models for Human Motion
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120
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IROS
2009
IEEE
138views Robotics» more  IROS 2009»
15 years 6 months ago
Using eigenposes for lossless periodic human motion imitation
— Programming a humanoid robot to perform an action that takes the robot’s complex dynamics into account is a challenging problem. Traditional approaches typically require high...
Rawichote Chalodhorn, Rajesh P. N. Rao
IROS
2008
IEEE
191views Robotics» more  IROS 2008»
15 years 6 months ago
Local Gaussian process regression for real-time model-based robot control
— High performance and compliant robot control requires accurate dynamics models which cannot be obtained analytically for sufficiently complex robot systems. In such cases, mac...
Duy Nguyen-Tuong, Jan Peters
ICML
2008
IEEE
16 years 17 days ago
Topologically-constrained latent variable models
In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion...
Raquel Urtasun, David J. Fleet, Andreas Geiger, Jo...
111
Voted
CVPR
2010
IEEE
15 years 8 months ago
Super-Resolution of Range Data in Dynamic Environments Using a Gaussian Framework
We present a flexible method for fusing information from optical and range sensors based on an accelerated highdimensional filtering approach. Our system takes as input a sequen...
Jennifer Dolson, Jongmin Baek, Christian Plagemann...
AROBOTS
2011
14 years 6 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox