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NIPS
2008
15 years 3 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
AROBOTS
2011
14 years 9 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
SC
1995
ACM
15 years 5 months ago
Predicting Application Behavior in Large Scale Shared-memory Multiprocessors
In this paper we present an analytical-based framework for parallel program performance prediction. The main thrust of this work is to provide a means for treating realistic appli...
Karim Harzallah, Kenneth C. Sevcik
KI
2007
Springer
15 years 8 months ago
Emotion Based Control Architecture for Robotics Applications
Assistance and service systems are one of the main research topics in robotics today. A major problem for creating these systems is that they have to work and navigate in the real ...
Jochen Hirth, Tim Braun, Karsten Berns
ICML
2010
IEEE
15 years 3 months ago
Modeling Interaction via the Principle of Maximum Causal Entropy
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
Brian Ziebart, J. Andrew Bagnell, Anind K. Dey