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Towards Interpretable Visuo-Tactile Predictive Models for Soft Robot Interactions

Donato, Enrico; Thuruthel, Thomas George; Falotico, Egidio; (2024) Towards Interpretable Visuo-Tactile Predictive Models for Soft Robot Interactions. In: 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). (pp. pp. 1567-1573). IEEE: Heidelberg, Germany. Green open access

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Abstract

Autonomous systems face the intricate challenge of navigating unpredictable environments and interacting with ex-ternal objects. The successful integration of robotic agents into real-world situations hinges on their perception capabilities, which involve amalgamating world models and predictive skills. Effective perception models build upon the fusion of various sensory modalities to probe the surroundings. Deep learning applied to raw sensory modalities offers a viable option. However, learning-based perceptive representations become difficult to interpret. This challenge is particularly pronounced in soft robots, where the compliance of structures and materials makes prediction even harder. Our work addresses this complexity by harnessing a generative model to construct a multi-modal perception model for soft robots and to leverage proprioceptive and visual information to anticipate and interpret contact interactions with external objects. A suite of tools to interpret the perception model is furnished, shedding light on the fusion and prediction processes across multiple sensory inputs after the learning phase. We will delve into the outlooks of the perception model and its implications for control purposes.

Type: Proceedings paper
Title: Towards Interpretable Visuo-Tactile Predictive Models for Soft Robot Interactions
Event: 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)
Dates: 1 Sep 2024 - 4 Sep 2024
ISBN-13: 979-8-3503-8652-3
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/BioRob60516.2024.10719859
Publisher version: https://doi.org/10.1109/biorob60516.2024.10719859
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Soft Robotics, Perception, Multi-modal Learning, Generative model, XAI
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10207445
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