---
_id: '6888'
abstract:
- lang: eng
  text: In this paper, we design novel liquid time-constant recurrent neural networks
    for robotic control, inspired by the brain of the nematode, C. elegans. In the
    worm's nervous system, neurons communicate through nonlinear time-varying synaptic
    links established amongst them by their particular wiring structure. This property
    enables neurons to express liquid time-constants dynamics and therefore allows
    the network to originate complex behaviors with a small number of neurons. We
    identify neuron-pair communication motifs as design operators and use them to
    configure compact neuronal network structures to govern sequential robotic tasks.
    The networks are systematically designed to map the environmental observations
    to motor actions, by their hierarchical topology from sensory neurons, through
    recurrently-wired interneurons, to motor neurons. The networks are then parametrized
    in a supervised-learning scheme by a search-based algorithm. We demonstrate that
    obtained networks realize interpretable dynamics. We evaluate their performance
    in controlling mobile and arm robots, and compare their attributes to other artificial
    neural network-based control agents. Finally, we experimentally show their superior
    resilience to environmental noise, compared to the existing machine learning-based
    methods.
alternative_title:
- ICRA
article_number: '8793840'
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: Manuel
  full_name: Zimmer, Manuel
  last_name: Zimmer
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000−0002−2985−7724
- first_name: Radu
  full_name: Grosu, Radu
  last_name: Grosu
citation:
  ama: 'Lechner M, Hasani R, Zimmer M, Henzinger TA, Grosu R. Designing worm-inspired
    neural networks for interpretable robotic control. In: <i>Proceedings - IEEE International
    Conference on Robotics and Automation</i>. Vol 2019-May. IEEE; 2019. doi:<a href="https://doi.org/10.1109/icra.2019.8793840">10.1109/icra.2019.8793840</a>'
  apa: 'Lechner, M., Hasani, R., Zimmer, M., Henzinger, T. A., &#38; Grosu, R. (2019).
    Designing worm-inspired neural networks for interpretable robotic control. In
    <i>Proceedings - IEEE International Conference on Robotics and Automation</i>
    (Vol. 2019–May). Montreal, QC, Canada: IEEE. <a href="https://doi.org/10.1109/icra.2019.8793840">https://doi.org/10.1109/icra.2019.8793840</a>'
  chicago: Lechner, Mathias, Ramin Hasani, Manuel Zimmer, Thomas A Henzinger, and
    Radu Grosu. “Designing Worm-Inspired Neural Networks for Interpretable Robotic
    Control.” In <i>Proceedings - IEEE International Conference on Robotics and Automation</i>,
    Vol. 2019–May. IEEE, 2019. <a href="https://doi.org/10.1109/icra.2019.8793840">https://doi.org/10.1109/icra.2019.8793840</a>.
  ieee: M. Lechner, R. Hasani, M. Zimmer, T. A. Henzinger, and R. Grosu, “Designing
    worm-inspired neural networks for interpretable robotic control,” in <i>Proceedings
    - IEEE International Conference on Robotics and Automation</i>, Montreal, QC,
    Canada, 2019, vol. 2019–May.
  ista: 'Lechner M, Hasani R, Zimmer M, Henzinger TA, Grosu R. 2019. Designing worm-inspired
    neural networks for interpretable robotic control. Proceedings - IEEE International
    Conference on Robotics and Automation. ICRA: International Conference on Robotics
    and Automation, ICRA, vol. 2019–May, 8793840.'
  mla: Lechner, Mathias, et al. “Designing Worm-Inspired Neural Networks for Interpretable
    Robotic Control.” <i>Proceedings - IEEE International Conference on Robotics and
    Automation</i>, vol. 2019–May, 8793840, IEEE, 2019, doi:<a href="https://doi.org/10.1109/icra.2019.8793840">10.1109/icra.2019.8793840</a>.
  short: M. Lechner, R. Hasani, M. Zimmer, T.A. Henzinger, R. Grosu, in:, Proceedings
    - IEEE International Conference on Robotics and Automation, IEEE, 2019.
conference:
  end_date: 2019-05-24
  location: Montreal, QC, Canada
  name: 'ICRA: International Conference on Robotics and Automation'
  start_date: 2019-05-20
date_created: 2019-09-18T08:09:51Z
date_published: 2019-05-01T00:00:00Z
date_updated: 2025-09-10T10:42:55Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1109/icra.2019.8793840
external_id:
  isi:
  - '000494942300011'
file:
- access_level: open_access
  checksum: f5545a6b60c3ffd01feb3613f81d03b6
  content_type: application/pdf
  creator: dernst
  date_created: 2020-10-08T17:30:38Z
  date_updated: 2020-10-08T17:30:38Z
  file_id: '8636'
  file_name: 2019_ICRA_Lechner.pdf
  file_size: 3265107
  relation: main_file
  success: 1
file_date_updated: 2020-10-08T17:30:38Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '05'
oa: 1
oa_version: Submitted Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: Z211
  name: Formal methods for the design and analysis of complex systems
publication: Proceedings - IEEE International Conference on Robotics and Automation
publication_identifier:
  isbn:
  - '9781538660270'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Designing worm-inspired neural networks for interpretable robotic control
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 2019-May
year: '2019'
...