Real-time deep learning of robotic manipulator inverse dynamics
Polydoros, A.S. ; Nalpantidis, L. and Kruger, V. LU
(2015)
IEEE/RSJ International Conference on Intelligent Robots and Systems , 2015
p.3442-3448
- Abstract
- In certain cases analytical derivation of physics-based models of robots is difficult or even impossible. A potential workaround is the approximation of robot models from sensor data-streams employing machine learning approaches. In this paper, the inverse dynamics models are learned by employing a novel real-time deep learning algorithm. The algorithm exploits the methods of self-organized learning, reservoir computing and Bayesian inference. It is evaluated and compared to other state of the art algorithms in terms of generalization ability, convergence and adaptability using five datasets gathered from four robots. Results show that the proposed algorithm can adapt to real-time changes of the inverse dynamics model significantly better... (More)
- In certain cases analytical derivation of physics-based models of robots is difficult or even impossible. A potential workaround is the approximation of robot models from sensor data-streams employing machine learning approaches. In this paper, the inverse dynamics models are learned by employing a novel real-time deep learning algorithm. The algorithm exploits the methods of self-organized learning, reservoir computing and Bayesian inference. It is evaluated and compared to other state of the art algorithms in terms of generalization ability, convergence and adaptability using five datasets gathered from four robots. Results show that the proposed algorithm can adapt to real-time changes of the inverse dynamics model significantly better than the other state of the art algorithms. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2dcf04e9-f812-42d8-b14c-7a804a0dd187
- author
- Polydoros, A.S.
; Nalpantidis, L.
and Kruger, V.
LU
- publishing date
- 2015-09-28
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- keywords
- approximation theory, belief networks, inference mechanisms, learning (artificial intelligence), real-time systems, Bayesian inference, machine learning, physics-based models, real-time deep learning, reservoir computing, robotic manipulator, inverse dynamics, self-organized learning, sensor data-streams, Adaptation models, computational modeling, Heuristic algorithms, Manipulator dynamics, Robot sensing systems
- host publication
- 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
- pages
- 7 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE/RSJ International Conference on Intelligent Robots and Systems , 2015
- conference location
- Hamburg, Germany
- conference dates
- 2015-09-28 - 2015-10-02
- external identifiers
-
- scopus:84958184203
- ISBN
- 978-1-4799-9994-1
- DOI
- 10.1109/IROS.2015.7353857
- language
- English
- LU publication?
- no
- id
- 2dcf04e9-f812-42d8-b14c-7a804a0dd187
- date added to LUP
- 2019-05-16 21:22:33
- date last changed
- 2022-04-26 00:00:01
@inproceedings{2dcf04e9-f812-42d8-b14c-7a804a0dd187,
abstract = {{In certain cases analytical derivation of physics-based models of robots is difficult or even impossible. A potential workaround is the approximation of robot models from sensor data-streams employing machine learning approaches. In this paper, the inverse dynamics models are learned by employing a novel real-time deep learning algorithm. The algorithm exploits the methods of self-organized learning, reservoir computing and Bayesian inference. It is evaluated and compared to other state of the art algorithms in terms of generalization ability, convergence and adaptability using five datasets gathered from four robots. Results show that the proposed algorithm can adapt to real-time changes of the inverse dynamics model significantly better than the other state of the art algorithms.}},
author = {{Polydoros, A.S. and Nalpantidis, L. and Kruger, V.}},
booktitle = {{2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),}},
isbn = {{978-1-4799-9994-1}},
keywords = {{approximation theory; belief networks; inference mechanisms; learning (artificial intelligence); real-time systems; Bayesian inference; machine learning; physics-based models; real-time deep learning; reservoir computing; robotic manipulator; inverse dynamics; self-organized learning; sensor data-streams; Adaptation models; computational modeling; Heuristic algorithms; Manipulator dynamics; Robot sensing systems}},
language = {{eng}},
month = {{09}},
pages = {{3442--3448}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Real-time deep learning of robotic manipulator inverse dynamics}},
url = {{http://dx.doi.org/10.1109/IROS.2015.7353857}},
doi = {{10.1109/IROS.2015.7353857}},
year = {{2015}},
}