---
_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: <i>Proceedings
    - IEEE International Conference on Robotics and Automation</i>. IEEE; 2020:5446-5452.
    doi:<a href="https://doi.org/10.1109/ICRA40945.2020.9196608">10.1109/ICRA40945.2020.9196608</a>'
  apa: 'Lechner, M., Hasani, R., Rus, D., &#38; Grosu, R. (2020). Gershgorin loss
    stabilizes the recurrent neural network compartment of an end-to-end robot learning
    scheme. In <i>Proceedings - IEEE International Conference on Robotics and Automation</i>
    (pp. 5446–5452). Paris, France: IEEE. <a href="https://doi.org/10.1109/ICRA40945.2020.9196608">https://doi.org/10.1109/ICRA40945.2020.9196608</a>'
  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 <i>Proceedings - IEEE International Conference on Robotics
    and Automation</i>, 5446–52. IEEE, 2020. <a href="https://doi.org/10.1109/ICRA40945.2020.9196608">https://doi.org/10.1109/ICRA40945.2020.9196608</a>.
  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 <i>Proceedings - IEEE International Conference on Robotics and Automation</i>,
    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.” <i>Proceedings - IEEE International
    Conference on Robotics and Automation</i>, IEEE, 2020, pp. 5446–52, doi:<a href="https://doi.org/10.1109/ICRA40945.2020.9196608">10.1109/ICRA40945.2020.9196608</a>.
  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'
...