Learning Probabilistic Models for Mobile Manipulation Robots
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3131-3134.
Abstract
Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. In this paper, we present novel approaches to allow mobile maniplation systems to autonomously adapt to new or changing situations. The approaches developed in this paper cover the following four topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment visual perception, and (4) learning novel manipulation tasks from human demonstrations.
@inproceedings{ijcai2013p471,
title = {Learning Probabilistic Models for Mobile Manipulation Robots},
author = {Sturm, Jürgen and Burgard, Wolfram},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3131--3134},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/471},
}
TY - CPAPER TI - Learning Probabilistic Models for Mobile Manipulation Robots AU - Sturm, Jürgen AU - Burgard, Wolfram T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3131 EP - 3134 UR - https://ijcai.org/proceedings/2013/471 ER -Copied
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