ShadowTac: Dense Measurement of Shear and Normal Deformation of a Tactile Membrane from Colored Shadows

Giuseppe Vitrani, Basile Pasquale, Michaël Wiertlewski*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

To robustly handle objects, robots must perceive mechanical interactions through touch with sufficient richness. New tactile sensors leverage miniature cameras to provide dense measurements of these interactions, allowing for the extraction of material properties and frictional information. Among the plethora of solutions, retrographic sensing is popular for its ability to finely resolve the shape of the object being touched. These sensors use a reflective membrane, illuminated at a shallow angle by three RGB lights from which fine details of the surface can be recovered. However, these retrographic sensors are unable to detect the lateral displacement of the membrane and, therefore overlook frictional information, which is crucial for grasping and manipulation. Embedding and tracking opaque markers has been a makeshift solution, but these markers occlude the membrane and are difficult to manufacture. In this paper, we introduce ShadowTac, a tactile sensor that combines retrographic illumination with non-intrusive markers created by colored shadows. We patterned the retrographic surface with a dense array of submillimeter dimples, which are small enough not to obstruct the view yet cast shadows large enough to be visible to the camera. ShadowTac captures a dense image of both the normal displacement field with fine details and a precise lateral displacement field by tracking the markers. Additionally, our sensor is easy to manufacture, as the dimple pattern can simply be molded. We evaluated the measurement reliability of ShadowTac and its effectiveness in estimating the incipient slip of arbitrary objects. The dense measurement of both the normal and shear deformation that the sensor captures makes it ideal for tracking dynamic interactions between robotic fingertips and manipulated objects.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Robotics and Automation, ICRA 2025
PublisherIEEE
Pages5004-5010
Number of pages7
ISBN (Electronic)979-8-3315-4139-2
DOIs
Publication statusPublished - 2025
Event2025 IEEE International Conference on Robotics and Automation, ICRA 2025 - Atlanta, United States
Duration: 19 May 202523 May 2025

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2025 IEEE International Conference on Robotics and Automation, ICRA 2025
Country/TerritoryUnited States
CityAtlanta
Period19/05/2523/05/25

Bibliographical note

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Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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