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.