System identification: theory for the user
System identification: theory for the user
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Style translation for human motion
ACM SIGGRAPH 2005 Papers
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Guided time warping for motion editing
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning for control from multiple demonstrations
Proceedings of the 25th international conference on Machine learning
Analytic moment-based Gaussian process filtering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficient failure detection on mobile robots using particle filters with Gaussian process proposals
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Target tracking without line of sight using range from radio
Autonomous Robots
Dynamic GP models: an overview and recent developments
ASM'12 Proceedings of the 6th international conference on Applied Mathematics, Simulation, Modelling
Gaussian Process Gauss-Newton for non-parametric simultaneous localization and mapping
International Journal of Robotics Research
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
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GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters. GP-BayesFilters have been shown to be extremely well suited for systems for which accurate parametric models are difficult to obtain. GP-BayesFilters learn non-parametric models from training data containing sequences of control inputs, observations, and ground truth states. The need for ground truth states limits the applicability of GP-BayesFilters to systems for which the ground truth can be estimated without significant overhead. In this paper we introduce GPBF-Learn, a framework for training GP-BayesFilters without ground truth states. Our approach extends Gaussian Process Latent Variable Models to the setting of dynamical robotics systems. We show how weak labels for the ground truth states can be incorporated into the GPBF-Learn framework. The approach is evaluated using a difficult tracking task, namely tracking a slotcar based on inertial measurement unit (IMU) observations only. We also show some special features enabled by this framework, including time alignment, and control replay for both the slotcar, and a robotic arm.