Learning Spatial Preconditions of Manipulation Skills using Random Forests

Oliver Kroemer and Gaurav S. Sukhatme
Conference Paper, International Conference on Humanoid Robots (Humanoids), January, 2016

Download Publication

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.

Abstract

Robots working in everyday and unstructured environments will need to perform manipulation skills using different sets of objects. To determine if a manipulation skill can be executed in a given situation, a robot will need to learn the preconditions of the skill. The robot will need to check both that the required objects are present in the scene and that they are arranged in a suitable manner for the skill to be executed. We propose a random forest approach to learn the set of spatial configurations of objects that fulfill a skill’s preconditions. We also explore how parts of objects and interactions between parts can be incorporated into the scene models to improve the generalization performance. The proposed approach was evaluated on the preconditions of six manipulation skills. The experiments show that using the ensemble approach, and including the parts and interactions, results in an increase in accuracy of 16.4%.


@conference{Kroemer-2016-112203,
author = {Oliver Kroemer and Gaurav S. Sukhatme},
title = {Learning Spatial Preconditions of Manipulation Skills using Random Forests},
booktitle = {International Conference on Humanoid Robots (Humanoids)},
year = {2016},
month = {January},
} 2019-03-12T14:04:52-04:00