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Title: Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces from Images
The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we introduce a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.  more » « less
Award ID(s):
1715195
NSF-PAR ID:
10292301
Author(s) / Creator(s):
Date Published:
Journal Name:
European Conference on Computer Vision
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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