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» Gaussian Process Dynamical Models for Human Motion
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IROS
2009
IEEE
138views Robotics» more  IROS 2009»
16 years 15 days 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
168
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IROS
2008
IEEE
191views Robotics» more  IROS 2008»
16 years 7 days 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 6 months 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...
CVPR
2010
IEEE
16 years 2 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
15 years 27 days 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