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IROS
2007
IEEE
168views Robotics» more  IROS 2007»
16 years 12 days ago
Improving humanoid locomotive performance with learnt approximated dynamics via Gaussian processes for regression
Abstract— We propose to improve the locomotive performance of humanoid robots by using approximated biped stepping and walking dynamics with reinforcement learning (RL). Although...
Jun Morimoto, Christopher G. Atkeson, Gen Endo, Go...
184
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ISVC
2009
Springer
16 years 20 days ago
Speech-Driven Facial Animation Using a Shared Gaussian Process Latent Variable Model
Abstract. In this work, synthesis of facial animation is done by modelling the mapping between facial motion and speech using the shared Gaussian process latent variable model. Bot...
Salil Deena, Aphrodite Galata
CVPR
2009
IEEE
17 years 1 months ago
Nonrigid Shape Recovery by Gaussian Process Regression
Most state-of-the-art nonrigid shape recovery methods usually use explicit deformable mesh models to regularize surface deformation and constrain the search space. These triangu...
Jianke Zhu, Michael R. Lyu, Steven C. H. Hoi
PAMI
2006
147views more  PAMI 2006»
15 years 6 months ago
Bayesian Gaussian Process Classification with the EM-EP Algorithm
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
Hyun-Chul Kim, Zoubin Ghahramani
AROBOTS
2011
15 years 1 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