Automatica (Journal of IFAC)
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Applied Intelligence
A tutorial on support vector regression
Statistics and Computing
Incremental Online Learning in High Dimensions
Neural Computation
Constructive Incremental Learning from Only Local Information
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Learning to Control in Operational Space
International Journal of Robotics Research
Operational Space Control: A Theoretical and Empirical Comparison
International Journal of Robotics Research
Sparse online model learning for robot control with support vector regression
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
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Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship of a manipulator, eliminating the need for any knowledge of the system model. Ideally, such algorithms should be able to process large amounts of data in an online and incremental manner, thus allowing the system to adapt to changes in its model structure or parameters. LocallyWeighted Projection Regression (LWPR) and other non-parametric regression techniques have been applied to learn manipulator inverse dynamics. However, a common issue amongst these learning algorithms is that the system is unable to generalize well outside of regions where it has been trained. Furthermore, learning commences entirely from 'scratch,' making no use of any a-priori knowledge which may be available. In this paper, an online, incremental learning algorithm incorporating prior knowledge is proposed. Prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the available prior information. It is shown that the proposed approach allows the system to operate well even without any initial training data, and further improves performance with additional online training.