Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Applied Intelligence
Feedback error learning and nonlinear adaptive control
Neural Networks
Incremental Online Learning in High Dimensions
Neural Computation
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Dynamic GP models: an overview and recent developments
ASM'12 Proceedings of the 6th international conference on Applied Mathematics, Simulation, Modelling
Regressing Local to Global Shape Properties for Online Segmentation and Tracking
International Journal of Computer Vision
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For many applications such as compliant, accurate robot tracking control, dynamics models learned from data can help to achieve both compliant control performance as well as high tracking quality. Online learning of these dynamics models allows the robot controller to adapt itself to changes in the dynamics (e.g., due to time-variant nonlinearities or unforeseen loads). However, online learning in real-time applications - as required in control - cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for fast real-time model learning. The proposed approach employs a sparsification method based on an independence measure. In combination with an incremental learning approach such as incremental Gaussian process regression, we obtain a model approximation method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real Barrett WAM robot demonstrates the applicability of the approach in real-time online model learning for real world systems.