Learning stable vector fields on Lie Groups
Urain, Julen ; Tateo, Davide LU
and Peters, Jan
(2022)
In IEEE Robotics and Automation Letters
7(4).
p.12569-12576
- Abstract
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector fields for orientations requires modeling the dynamics in non-Euclidean manifolds, such as Lie Groups. In this paper, we present a novel vector field model that can guarantee most of the properties of previous approaches i.e., stability, smoothness, and reactivity beyond the Euclidean space. In the experimental evaluation, we show the... (More)
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector fields for orientations requires modeling the dynamics in non-Euclidean manifolds, such as Lie Groups. In this paper, we present a novel vector field model that can guarantee most of the properties of previous approaches i.e., stability, smoothness, and reactivity beyond the Euclidean space. In the experimental evaluation, we show the performance of our proposed vector field model to learn stable vector fields for full robot poses as SE(2) and SE(3) in both simulated and real robotics tasks.
(Less)
- author
- Urain, Julen
; Tateo, Davide
LU
and Peters, Jan
- publishing date
- 2022-10-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Imitation learning, learning from demonstration, lie groups, machine learning for robot control, reactive motion generation
- in
- IEEE Robotics and Automation Letters
- volume
- 7
- issue
- 4
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85141552558
- ISSN
- 2377-3766
- DOI
- 10.1109/LRA.2022.3219019
- language
- English
- LU publication?
- no
- id
- 641f841b-26cc-461f-8031-70896532c915
- date added to LUP
- 2025-10-16 14:33:43
- date last changed
- 2025-10-21 08:26:19
@article{641f841b-26cc-461f-8031-70896532c915,
abstract = {{<p>Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that learning adaptive, reactive, smooth, and stable vector fields is possible. However, these approaches define vector fields on a flat Euclidean manifold, while representing vector fields for orientations requires modeling the dynamics in non-Euclidean manifolds, such as Lie Groups. In this paper, we present a novel vector field model that can guarantee most of the properties of previous approaches i.e., stability, smoothness, and reactivity beyond the Euclidean space. In the experimental evaluation, we show the performance of our proposed vector field model to learn stable vector fields for full robot poses as SE(2) and SE(3) in both simulated and real robotics tasks.</p>}},
author = {{Urain, Julen and Tateo, Davide and Peters, Jan}},
issn = {{2377-3766}},
keywords = {{Imitation learning; learning from demonstration; lie groups; machine learning for robot control; reactive motion generation}},
language = {{eng}},
month = {{10}},
number = {{4}},
pages = {{12569--12576}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Robotics and Automation Letters}},
title = {{Learning stable vector fields on Lie Groups}},
url = {{http://dx.doi.org/10.1109/LRA.2022.3219019}},
doi = {{10.1109/LRA.2022.3219019}},
volume = {{7}},
year = {{2022}},
}