Learning the visual–oculomotor transformation: effects on saccade control and space representation

Article

Antonelli, M., Duran, A., Chinellato, E. and Del Pobil, A. 2015. Learning the visual–oculomotor transformation: effects on saccade control and space representation. Robotics and Autonomous Systems. 71, pp. 13-22. https://doi.org/10.1016/j.robot.2014.11.018
TypeArticle
TitleLearning the visual–oculomotor transformation: effects on saccade control and space representation
AuthorsAntonelli, M., Duran, A., Chinellato, E. and Del Pobil, A.
Abstract

Active eye movements can be exploited to build a visuomotor representation of the surrounding environment. Maintaining and improving such representation requires to update the internal model involved in the generation of eye movements. From this perspective, action and perception are thus tightly coupled and interdependent. In this work, we encoded the internal model for oculomotor control with an adaptive filter inspired by the functionality of the cerebellum. Recurrent loops between a feed-back controller and the internal model allow our system to perform accurate binocular saccades and create an implicit representation of the nearby space. Simulation results show that this recurrent architecture outperforms classical feedback-error-learning in terms of both accuracy and sensitivity to system parameters. The proposed approach was validated implementing the framework on an anthropomorphic robotic head.

PublisherElsevier
JournalRobotics and Autonomous Systems
ISSN0921-8890
Publication dates
Online29 Dec 2014
Print01 Sep 2015
Publication process dates
Deposited10 May 2016
Accepted26 Nov 2014
Output statusPublished
Accepted author manuscript
License
Copyright Statement

© 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Additional information

September 2015, Emerging Spatial Competences: From Machine Perception to Sensorimotor Intelligence

Digital Object Identifier (DOI)https://doi.org/10.1016/j.robot.2014.11.018
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/8661x

Log in to edit

Download files

Accepted author manuscript
ras_2014.pdf
License: CC BY-NC-ND 4.0

  • 27
    total views
  • 12
    total downloads
  • 1
    views this month
  • 3
    downloads this month

Export as

Related outputs

Affective visuomotor interaction: a functional model for socially competent robot grasping
Chinellato, E., Ferretti, G. and Irving, L. 2019. Affective visuomotor interaction: a functional model for socially competent robot grasping. Martinez-Hernandez, U., Vouloutsi, V., Mura, A., Mangan, M., Minoru, A., Prescott, T. and Verschure, P. (ed.) 8th International Conference, Living Machines 2019. Nara, Japan 09 - 12 Jul 2019 Springer, Cham. pp. 51-62 https://doi.org/10.1007/978-3-030-24741-6_5
The competitive and multi-faceted nature of neural coding in motor imagery: Comment on "Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics" by V. Mohan et al.
Chinellato, E. 2019. The competitive and multi-faceted nature of neural coding in motor imagery: Comment on "Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics" by V. Mohan et al. Physics of life reviews. https://doi.org/10.1016/j.plrev.2019.02.003
Advances in human-computer interactions: methods, algorithms, and applications
Solari, F., Chessa, M., Chinellato, E. and Bresciani, J. 2018. Advances in human-computer interactions: methods, algorithms, and applications. Computational Intelligence and Neuroscience. 2018. https://doi.org/10.1155/2018/4127475
Feature space analysis for human activity recognition in smart environments
Chinellato, E., Hogg, D. and Cohn, A. 2016. Feature space analysis for human activity recognition in smart environments. 12th International Conference on Intelligent Environments (IE). London, United Kingdom 14 - 16 Sep 2016 Institute of Electrical and Electronics Engineers (IEEE). pp. 194-197 https://doi.org/10.1109/IE.2016.43
Sensorial computing
Varsani, P., Moseley, R., Jones, S., James-Reynolds, C., Chinellato, E. and Augusto, J. 2018. Sensorial computing. in: Filimowicz, M. and Tzankova, V. (ed.) New Directions in Third Wave Human-Computer Interaction: Volume 1 - Technologies Cham, Switzerland Springer. pp. 265-284
The STRANDS project: long-term autonomy in everyday environments
Hawes, N., Burbridge, C., Jovan, F., Kunze, L., Lacerda, B., Mudrova, L., Young, J., Wyatt, J., Hebesberger, D., Kortner, T., Ambrus, R., Bore, N., Folkesson, J., Jensfelt, P., Beyer, L., Hermans, A., Leibe, B., Aldoma, A., Faulhammer, T., Zillich, M., Vincze, M., Chinellato, E., Al-Omari, M., Duckworth, P., Gatsoulis, Y., Hogg, D., Cohn, A., Dondrup, C., Pulido Fentanes, J., Krajnik, T., Santos, J., Duckett, T. and Hanheide, M. 2017. The STRANDS project: long-term autonomy in everyday environments. IEEE Robotics & Automation Magazine. 24 (3), pp. 146-156. https://doi.org/10.1109/MRA.2016.2636359
Decoding information for grasping from the macaque dorsomedial visual stream
Filippini, M., Breveglieri, R., Akhras, M., Bosco, A., Chinellato, E. and Fattori, P. 2017. Decoding information for grasping from the macaque dorsomedial visual stream. The Journal of Neuroscience. 37 (16), pp. 4311-4322. https://doi.org/10.1523/JNEUROSCI.3077-16.2017
An incremental von mises mixture framework for modelling human activity streaming data
Chinellato, E., Mardia, K., Hogg, D. and Cohn, A. 2017. An incremental von mises mixture framework for modelling human activity streaming data. International Work-Conference on Time Series Analysis (ITISE 2017). Granada, Spain 18 - 20 Sep 2017 pp. 379-389
Adaptive saccade controller inspired by the primates' cerebellum
Antonelli, M., Duran, A., Chinellato, E. and Del Pobil, A. 2015. Adaptive saccade controller inspired by the primates' cerebellum. IEEE International Conference on Robotics and Automation (ICRA). Seattle, Washington, USA 26 - 30 May 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 5048-5053 https://doi.org/10.1109/ICRA.2015.7139901
Motor interference in interactive contexts
Chinellato, E., Castiello, U. and Sartori, L. 2015. Motor interference in interactive contexts. Frontiers in Psychology. 6. https://doi.org/10.3389/fpsyg.2015.00791
The visual neuroscience of robotic grasping: achieving sensorimotor skills through dorsal-ventral stream integration
Chinellato, E. and Del Pobil, A. 2016. The visual neuroscience of robotic grasping: achieving sensorimotor skills through dorsal-ventral stream integration. Springer.
Unsupervised grounding of textual descriptions of object features and actions in video
Alomari, M., Chinellato, E., Gatsoulis, Y., Hogg, D. and Cohn, A. 2016. Unsupervised grounding of textual descriptions of object features and actions in video. 15th International Conference Principles of Knowledge Representation and Reasoning (KR 2016). Cape Town, South Africa 25 - 29 Apr 2016 Association for the Advancement of Artificial Intelligence (AAAI). pp. 505-508
The multiform motor cortical output: kinematic, predictive and response coding
Sartori, L., Betti, S., Chinellato, E. and Castiello, U. 2015. The multiform motor cortical output: kinematic, predictive and response coding. Cortex. 70, pp. 169-178. https://doi.org/10.1016/j.cortex.2015.01.019
A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot
Antonelli, M., Gibaldi, A., Beuth, F., Duran, A., Canessa, A., Chessa, M., Solari, F., Del Pobil, A., Hamker, F., Chinellato, E. and Sabatini, S. 2014. A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot. IEEE Transactions on Autonomous Mental Development. 6 (4), pp. 259-273. https://doi.org/10.1109/TAMD.2014.2332875