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Sensors 2017, 17(11), 2455;

Inferring Interaction Force from Visual Information without Using Physical Force Sensors

Department of Software and Computer Engineering, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea
Department of Mechanical, Robotics and Energy Engineering, Dongguk University, 30, Pildong-ro 1gil, Jung-gu, Seoul 04620, Korea
Author to whom correspondence should be addressed.
Received: 13 August 2017 / Revised: 30 September 2017 / Accepted: 24 October 2017 / Published: 26 October 2017
(This article belongs to the Section Physical Sensors)
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In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors. View Full-Text
Keywords: deep learning; force estimation; interaction force; vision deep learning; force estimation; interaction force; vision

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Hwang, W.; Lim, S.-C. Inferring Interaction Force from Visual Information without Using Physical Force Sensors. Sensors 2017, 17, 2455.

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