A Library for Locally Weighted Projection Regression (2009)
Stefan Klanke, Sethu Vijayakumar, Stefan Schaal, Soeren Sonnenburg
In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the...
Multi-task Gaussian process learning of robot inverse dynamics (2009)
Chai, Kian Ming, Williams, Christopher, Klanke, Stefan, Vijayakumar, Sethu
The inverse dynamics problem for a robotic manipulator is to compute the torques needed at the joints to drive it along a given trajectory; it is beneficial to be able to learn this function for...
Does Dimensionality Reduction Improve the Quality of Motion Interpolation? (2009)
Bitzer, Sebastian, Klanke, Stefan, Vijayakumar, Sethu
In recent years nonlinear dimensionality reduction has frequently been suggested for the modelling of high-dimensional motion data. While it is intuitively plausible to use dimensionality reduction...
Multi-task Gaussian Process Learning of Robot Inverse Dynamics (2009)
Chai, Kian Ming, Williams, Christopher, Klanke, Stefan, Vijayakumar, Sethu
The inverse dynamics problem for a robotic manipulator is to compute the torques needed at the joints to drive it along a given trajectory; it is beneficial to be able to learn this function for...
A Novel Method for Learning Policies from Constrained Motion (2009)
Howard, Matthew, Klanke, Stefan, Gienger, Michael, Görick, Christian, Vijayakumar, Sethu
Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between...
Howard, Matthew, Klanke, Stefan, Gienger, Michael, Görick, Christian, Vijayakumar, Sethu
Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high dimensional movement systems like humanoid robots. We...
Abstract- We present an extension to the Parametrized Self-Organizing Map that allows the construction of continuous manifolds from noisy, incomplete and not necessarily gridorganized training data....
A Leave-K-Out Cross-Validation Scheme for Unsupervised Kernel Regression (2008)
Stefan Klanke, Helge Ritter, Neuroinformatics Group
We show how to employ leave-K-out cross-validation in Unsupervised Kernel Regression, a recent method for learning of nonlinear manifolds. We thereby generalize an already present regularization...
Optimal Control with Adaptive Internal Dynamics Models (2008)
Mitrovic, Djordje, Klanke, Stefan, Vijayakumar, Sethu
Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. The optimal feedback control law for systems...
Adaptive Optimal Control for Redundantly Actuated Arms (2008)
Mitrovic, Djordje, Klanke, Stefan, Vijayakumar, Sethu
Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. Recent developments, such as the iterative...
Learning Potential-based Policies from Constrained Motion (2008)
Howard, Matthew, Klanke, Stefan, Gienger, Michael, Görick, Christian, Vijayakumar, Sethu
We present a method for learning potential-based policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a...
Learned system dynamics for adaptive optimal feedback control (2007)
Mitrovic, Djordje, Klanke, Stefan, Vijayakumar, Sethu
Optimal feedback control (OFC) has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. In contrast to classic open loop...
This thesis presents several new developments in the field of manifold learning and nonlinear dimensionality reduction. The main text can be divided into three parts, the first of which presents a...
A Library For Locally Weighted Projection Regression (2007)
Klanke, Stefan, Vijayakumar, Sethu, Schaal, Stefan
In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the...
Variants of Unsupervised Kernel Regression: General Cost Functions (2007)
We present an extension to unsupervised kernel regression (UKR), a recent method for learning of nonlinear manifolds, which can utilize leave-one-out cross-validation as an automatic complexity...
This thesis presents several new developments in the field of manifold learning and nonlinear dimensionality reduction. The main text can be divided into three parts, the first of which presents a...
Contents A Library for Locally Weighted Projection Regression — Supplementary Documentation — (2007)
Dynamic Path Planning for a 7-DOF Robot Arm (2006)
Klanke, Stefan, Lebedev, Dimitry, Haschke, Robert, Steil, Jochen, Ritter, Helge
We present an on-line, robust, and efficient path planner for the redundant Mitsubishi PA-10 arm with 7 degrees of freedom (DOF) in non-stationary environments. Because of the specific kinematic...
A Leave-K-Out Cross-Validation Scheme for Unsupervised Kernel Regression (2006)
We show how to employ leave-K-out cross-validation in Unsupervised Kernel Regression, a recent method for learning of nonlinear manifolds. We thereby generalize an already present regularization...
Variants of Unsupervised Kernel Regression: General Cost Functions (2006)
We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the epsilon-insensitive loss and visually demonstrate...
H.: Variants of Unsupervised Kernel Regression: General loss functions (2006)
Abstract. We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the ɛ-insensitive loss and visually...
Principal Surfaces from Unsupervised Kernel Regression (2005)
Meinicke, Peter, Klanke, Stefan, Memisevic, Roland, Ritter, Helge
We propose a nonparametric approach to learning of principal surfaces based on an unsupervised formulation of the Nadaraya-Watson kernel regression estimator. As compared with previous approaches to...
PSOM+ : Parametrized Self-Organizing Maps for noisy and incomplete data (2005)
We present an extension to the Parametrized Self-Organizing Map that allows the construction of continuous manifolds from noisy, incomplete and not necessarily gridorganized training data. All three...