Learning non-holonomic object models for mobile manipulation
Jonathan Scholz, Martin Levihn, Charles L. Isbell, Henrik Christensen, Mike Stilman
Abstract
For a mobile manipulator to interact with large everyday objects, such as office tables, it is often important to have dynamic models of these objects. However, as it is infeasible to provide the robot with models for every possible object it may encounter, it is desirable that the robot can identify common object models autonomously. Existing methods for addressing this challenge are limited by being either purely kinematic, or inefficient due to a lack of physical structure. In this paper, we present a physics-based method for estimating the dynamics of common non-holonomic objects using a mobile manipulator, and demonstrate its efficiency compared to existing approaches.
BibTeX
@inproceedings{icra2015_learningnonholon,
title = {Learning non-holonomic object models for mobile manipulation},
author = {Jonathan Scholz and Martin Levihn and Charles L. Isbell and Henrik Christensen and Mike Stilman},
booktitle = {ICRA 2015},
year = {2015}
}