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NIPS
2008
13 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
IROS
2008
IEEE
191views Robotics» more  IROS 2008»
13 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
13 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...
ICRA
2008
IEEE
169views Robotics» more  ICRA 2008»
13 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»
13 years 9 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