About
Function approximation from noisy data is a central task in robot learning. Relevant problems include sensor modeling, manipulation, control, and many others. A large number of function approximation methods have been proposed from statistics, machine learning, and control system theory to address robotics-related issues such as online updates, active sampling, high dimensionality, non-homogeneous noise, and missing features.
In this workshop, we would like to develop a common understanding of the benefits and drawbacks of different function approximation approaches and to derive practical guidelines for selecting a suitable approach to a given problem.
In addition, we would like to discuss two key points of criticism in current robot learning research. First, data-driven machine learning methods do, in fact, not necessarily outperform models designed by human experts and we would like to explore what function approximation problems in robotics really have to be learned. Second, function approximation/regression methods are typically evaluated using different metrics and data sets, making standardized comparisons challenging.
For more information visit the Workshop website.
Videos
Lectures

Going forward with Probablistic Local Learning Approaches
Aug 3, 2009
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4298 views

Optimized Information Gatheringin Robotics and Sensor Networks
Aug 3, 2009
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3786 views

Gaussian processes for Bayesian Filtering
Aug 3, 2009
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4205 views

Learning vehicular dynamics models with application to helicopter modeling and c...
Aug 3, 2009
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4494 views

Function Approximation for Imitation Learning in Humanoid Robots
Aug 3, 2009
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3596 views

The Role of Function Approximation for both Regression and Classifiction in Robo...
Aug 3, 2009
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3561 views

Reinforcement Learning by Reward-Weighted Regression
Aug 3, 2009
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4479 views

Adaptation and Self-Supervision in Mobile Robots Poster Spotlight Presentations
Aug 3, 2009
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3092 views
Panel Session

Panel Discussion
Aug 3, 2009
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4671 views