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NIPS
2008
15 years 6 months ago
Local Gaussian Process Regression for Real Time Online Model Learning
Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by...
Duy Nguyen-Tuong, Matthias Seeger, Jan Peters
153
Voted
IROS
2008
IEEE
191views Robotics» more  IROS 2008»
15 years 11 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
NIPS
2008
15 years 6 months ago
Bayesian Kernel Shaping for Learning Control
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakum...
152
Voted
ICRA
2008
IEEE
169views Robotics» more  ICRA 2008»
15 years 11 months ago
Sparse incremental learning for interactive robot control policy estimation
— We are interested in transferring control policies for arbitrary tasks from a human to a robot. Using interactive demonstration via teloperation as our transfer scenario, we ca...
Daniel H. Grollman, Odest Chadwicke Jenkins
CDC
2009
IEEE
157views Control Systems» more  CDC 2009»
15 years 10 months ago
On trajectory optimization for active sensing in Gaussian process models
Abstract— We consider the problem of optimizing the trajectory of a mobile sensor with perfect localization whose task is to estimate a stochastic, perhaps multidimensional fiel...
Jerome Le Ny, George J. Pappas