S
Stefan Klanke
Researcher at University of Edinburgh
Publications - 36
Citations - 976
Stefan Klanke is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Optimal control & Humanoid robot. The author has an hindex of 16, co-authored 36 publications receiving 937 citations. Previous affiliations of Stefan Klanke include Bielefeld University & Radboud University Nijmegen.
Papers
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Proceedings Article
Multi-task Gaussian Process Learning of Robot Inverse Dynamics
TL;DR: A multi-task Gaussian process prior for handling multiple loads, where the inter-task similarity depends on the underlying inertial parameters, and generally improves performance over either learning only on single tasks or pooling the data over all tasks.
Book ChapterDOI
Adaptive Optimal Feedback Control with Learned Internal Dynamics Models
TL;DR: This chapter combines the ILQG framework with learning the forward dynamics for simulated arms, which exhibit large redundancies, both, in kinematics and in the actuation to demonstrate how the approach can compensate for complex dynamic perturbations in an online fashion.
Journal Article
A Library for Locally Weighted Projection Regression
TL;DR: An improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data and provides wrappers for several programming languages.
Journal ArticleDOI
Principal surfaces from unsupervised kernel regression
TL;DR: This work proposes a nonparametric approach to learning of principal surfaces based on an unsupervised formulation of the Nadaraya-Watson kernel regression estimator, which allows for a convenient incorporation of nonlinear spectral methods for parameter initialization, beyond classical initializations based on linear PCA.
Journal ArticleDOI
Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience.
Ole Jensen,Ali Bahramisharif,Robert Oostenveld,Stefan Klanke,Avgis Hadjipapas,Yuka Okazaki,Marcel A. J. van Gerven +6 more
TL;DR: It is argued that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will and hold the promise of providing new ways for investigating the brain at work.