Thuruthel, TG;
Iida, F;
(2023)
Multi-modal Sensor Fusion for Learning Rich Models for Interacting Soft Robots.
In:
2023 IEEE International Conference on Soft Robotics, RoboSoft 2023.
(pp. pp. 1327-1333).
IEEE: Singapore.
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Abstract
Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the self and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-modal Sensor Fusion for Learning Rich Models for Interacting Soft Robots |
| Event: | 2023 IEEE International Conference on Soft Robotics (RoboSoft), 03-07 April 2023, Singapore |
| Dates: | 3 Apr 2023 - 7 Apr 2023 |
| ISBN-13: | 9798350332223 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/RoboSoft55895.2023.10121992 |
| Publisher version: | https://doi.org/10.1109/RoboSoft55895.2023.1012199... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. - This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) RoboPatient grant EP/T00519X/1. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. |
| Keywords: | Computational modeling, Time series analysis, Computer architecture, Predictive models, Logic gates, Benchmark testing, Prediction algorithms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10171973 |
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