Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth function
Learning inverse kinematics has long been fascinating the robot learning community. While humans acq...
Model-based control is essential for compliant control and force control in many modern complex robo...
Abstract: Locally weighted learning (LWL) is a class of statistical learning techniques that provide...
this paper, we show how discontinuous switching between local regression models can be learned. The ...
An approach is presented to learning high dimensional functions in the case where the learning algor...
Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic ...
Abstract: We introduce a new algorithm, Locally Weighted Projection Regression (LWPR), for increment...
Learning inverse kinematics has long been fascinating the robot learning community. While humans acq...
While recent research in neural networks and statistical learning has focused mostly on learning fro...
Local Model Networks are hybrid models which allow the easy integration of a priori knowledge, as we...
The Local Model Networks (networks composed of locally accurate models, where the output is interpo...
Abstract. This paper surveys locally weighted learning, a form of lazy learning and memory-based lea...
Humanoid robots are high-dimensional movement systems for which analytical system identification and...
We construct models of the evolution of human learning on a visualmotor task by analysing a large se...
In this thesis a new supervised function approximation technique called Hierarchical Network of Loca...
Learning inverse kinematics has long been fascinating the robot learning community. While humans acq...
Model-based control is essential for compliant control and force control in many modern complex robo...
Abstract: Locally weighted learning (LWL) is a class of statistical learning techniques that provide...
this paper, we show how discontinuous switching between local regression models can be learned. The ...
An approach is presented to learning high dimensional functions in the case where the learning algor...
Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic ...
Abstract: We introduce a new algorithm, Locally Weighted Projection Regression (LWPR), for increment...
Learning inverse kinematics has long been fascinating the robot learning community. While humans acq...
While recent research in neural networks and statistical learning has focused mostly on learning fro...
Local Model Networks are hybrid models which allow the easy integration of a priori knowledge, as we...
The Local Model Networks (networks composed of locally accurate models, where the output is interpo...
Abstract. This paper surveys locally weighted learning, a form of lazy learning and memory-based lea...
Humanoid robots are high-dimensional movement systems for which analytical system identification and...
We construct models of the evolution of human learning on a visualmotor task by analysing a large se...
In this thesis a new supervised function approximation technique called Hierarchical Network of Loca...
Learning inverse kinematics has long been fascinating the robot learning community. While humans acq...
Model-based control is essential for compliant control and force control in many modern complex robo...
Abstract: Locally weighted learning (LWL) is a class of statistical learning techniques that provide...
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