---
_id: '8704'
abstract:
- lang: eng
text: Traditional robotic control suits require profound task-specific knowledge
for designing, building and testing control software. The rise of Deep Learning
has enabled end-to-end solutions to be learned entirely from data, requiring minimal
knowledge about the application area. We design a learning scheme to train end-to-end
linear dynamical systems (LDS)s by gradient descent in imitation learning robotic
domains. We introduce a new regularization loss component together with a learning
algorithm that improves the stability of the learned autonomous system, by forcing
the eigenvalues of the internal state updates of an LDS to be negative reals.
We evaluate our approach on a series of real-life and simulated robotic experiments,
in comparison to linear and nonlinear Recurrent Neural Network (RNN) architectures.
Our results show that our stabilizing method significantly improves test performance
of LDS, enabling such linear models to match the performance of contemporary nonlinear
RNN architectures. A video of the obstacle avoidance performance of our method
on a mobile robot, in unseen environments, compared to other methods can be viewed
at https://youtu.be/mhEsCoNao5E.
acknowledgement: M.L. is supported in parts by the Austrian Science Fund (FWF) under
grant Z211-N23 (Wittgenstein Award). R.H., and R.G. are partially supported by the
Horizon-2020 ECSELProject grant No. 783163 (iDev40), and the Austrian Research Promotion
Agency (FFG), Project No. 860424. R.H. and D.R. is partially supported by the Boeing
Company.
alternative_title:
- ICRA
article_processing_charge: No
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Lechner M, Hasani R, Rus D, Grosu R. Gershgorin loss stabilizes the recurrent
neural network compartment of an end-to-end robot learning scheme. In: Proceedings
- IEEE International Conference on Robotics and Automation. IEEE; 2020:5446-5452.
doi:10.1109/ICRA40945.2020.9196608'
apa: 'Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2020). Gershgorin loss
stabilizes the recurrent neural network compartment of an end-to-end robot learning
scheme. In Proceedings - IEEE International Conference on Robotics and Automation
(pp. 5446–5452). Paris, France: IEEE. https://doi.org/10.1109/ICRA40945.2020.9196608'
chicago: Lechner, Mathias, Ramin Hasani, Daniela Rus, and Radu Grosu. “Gershgorin
Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-End Robot
Learning Scheme.” In Proceedings - IEEE International Conference on Robotics
and Automation, 5446–52. IEEE, 2020. https://doi.org/10.1109/ICRA40945.2020.9196608.
ieee: M. Lechner, R. Hasani, D. Rus, and R. Grosu, “Gershgorin loss stabilizes the
recurrent neural network compartment of an end-to-end robot learning scheme,”
in Proceedings - IEEE International Conference on Robotics and Automation,
Paris, France, 2020, pp. 5446–5452.
ista: 'Lechner M, Hasani R, Rus D, Grosu R. 2020. Gershgorin loss stabilizes the
recurrent neural network compartment of an end-to-end robot learning scheme. Proceedings
- IEEE International Conference on Robotics and Automation. ICRA: International
Conference on Robotics and Automation, ICRA, , 5446–5452.'
mla: Lechner, Mathias, et al. “Gershgorin Loss Stabilizes the Recurrent Neural Network
Compartment of an End-to-End Robot Learning Scheme.” Proceedings - IEEE International
Conference on Robotics and Automation, IEEE, 2020, pp. 5446–52, doi:10.1109/ICRA40945.2020.9196608.
short: M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International
Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452.
conference:
end_date: 2020-08-31
location: Paris, France
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2020-05-31
date_created: 2020-10-25T23:01:19Z
date_published: 2020-05-01T00:00:00Z
date_updated: 2023-08-22T10:40:15Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1109/ICRA40945.2020.9196608
external_id:
isi:
- '000712319503110'
file:
- access_level: open_access
checksum: fccf7b986ac78046918a298cc6849a50
content_type: application/pdf
creator: dernst
date_created: 2020-11-06T10:58:49Z
date_updated: 2020-11-06T10:58:49Z
file_id: '8733'
file_name: 2020_ICRA_Lechner.pdf
file_size: 1070010
relation: main_file
success: 1
file_date_updated: 2020-11-06T10:58:49Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '05'
oa: 1
oa_version: Submitted Version
page: 5446-5452
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Proceedings - IEEE International Conference on Robotics and Automation
publication_identifier:
isbn:
- '9781728173955'
issn:
- '10504729'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end
robot learning scheme
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2020'
...