/Meta-level Priors for Learning Manipulation Skills with Sparse Features

Meta-level Priors for Learning Manipulation Skills with Sparse Features

Oliver Kroemer and Gaurav Sukhatme
Conference Paper, International Symposium on Experimental Robotics (ISER), January, 2016

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


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.

BibTeX Reference
author = {Oliver Kroemer and Gaurav Sukhatme},
title = {Meta-level Priors for Learning Manipulation Skills with Sparse Features},
booktitle = {International Symposium on Experimental Robotics (ISER)},
year = {2016},
month = {January},