Salazar, P.J. and Prescott, T.J. orcid.org/0000-0003-4927-5390 (2022) Tactile and proprioceptive online learning in robotic contour following. In: Pacheco-Gutierrez, S., Cryer, A., Caliskanelli, I., Tugal, H. and Skilton, R., (eds.) Towards Autonomous Robotic Systems: 23rd Annual Conference, TAROS 2022, Culham, UK, September 7–9, 2022, Proceedings. 23rd Annual Conference, TAROS 2022, 07-09 Sep 2022, Culham, UK. Springer International Publishing , pp. 166-178. ISBN 9783031159077
Abstract
Purposive and systematic movements are required for the exploration of tactile properties. Obtaining precise spatial details of the shape of an object with tactile data requires a dynamic edge following exploratory procedure. The contour following task relies on the perception of the angle and position of the sensor relative to the edge of the object. The perceived angle determines the direction of exploratory actions, and the position indicates the location relative to the edge for placing the sensor where the angle tends to be perceived more accurately. Differences in the consistency of the acquired tactile data during the execution of the task might induce inaccuracies in the predictions of the sensor model, and therefore impact on the enactment of active and exploratory movements. This work examines the influence of integrating information from robot proprioception to assess the accuracy of a Bayesian model and update its parameters to enhance the perception of angle and position of the sensor. The incorporation of proprioceptive information achieves an increased number of task completions relative to performing the task with a model trained with tactile data collected offline. Studies in biological touch suggest that tactile and proprioceptive information contribute synergistically to the perception of geometric properties and control of the sensory apparatus; this work proposes a method for the improvement of perception of the magnitudes required to actively follow the contour of an object under the presence of variability in the acquired tactile data.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in Towards Autonomous Robotic Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Neurosciences; Bioengineering; Clinical Research; Active touch; Online learning; Contour following; Exploratory procedure |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 813713 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Jan 2023 15:14 |
Last Modified: | 01 Sep 2023 00:13 |
Status: | Published |
Publisher: | Springer International Publishing |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-15908-4_14 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195288 |