Learning Kinematic Models for Articulated Objects
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1851-1855.
Abstract
Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings.Jürgen Sturm, Vijay Pradeep, Cyrill Stachniss, Christian Plagemann, Kurt Konolige, Wolfram Burgard
@inproceedings{ijcai2009p307,
title = {Learning Kinematic Models for Articulated Objects},
author = {Sturm, Jürgen and Pradeep, Vijay and Stachniss, Cyrill and Plagemann, Christian and Konolige, Kurt and Burgard, Wolfram},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1851--1855},
year = {2009},
url = {https://ijcai.org/proceedings/2009/307},
}
TY - CPAPER TI - Learning Kinematic Models for Articulated Objects AU - Sturm, Jürgen AU - Pradeep, Vijay AU - Stachniss, Cyrill AU - Plagemann, Christian AU - Konolige, Kurt AU - Burgard, Wolfram T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1851 EP - 1855 UR - https://ijcai.org/proceedings/2009/307 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: