Meta-level Priors for Learning Manipulation Skills with Sparse Features

Abstract

Manipulation skills need to adapt to the geometric features of the objects that they are manipulating, e.g. the position or length of an action-relevant part of an object. However, only a sparse set of the objects’ features will be relevant for generalizing the manipulation skill between different scenarios and objects. Rather than relying on a human to select the relevant features, our work focuses on incorporating feature selection into the skill learning process. An informative prior over the features’ relevance can guide the robot’s feature selection process. This prior is computed using a meta-level prior, which is learned from previous skills. The meta-level prior is transferred to new skills using meta features. Our robot experiments show that using a meta-level prior results in better generalization performance and more efficient skill learning.