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      Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images

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          Abstract

          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 develop a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.

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          Author and article information

          Journal
          29 April 2020
          Article
          2004.14487

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          19 pages, 5 figures, 6 tables
          cs.CV cs.LG cs.RO eess.IV

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