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Review Article | Open Access

Embodied tactile perception and learning

Huaping Liu1( ), Di Guo1, Fuchun Sun1, Wuqiang Yang2, Steve Furber3, Tengchen Sun4
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9 PL, U.K.
Department of Computer Science, The University of Manchester, Manchester M13 9 PL, U.K.
Beijing Tashan Technology Co., Ltd., Beijing 102300, China
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Abstract

Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large-scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

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Brain Science Advances
Pages 132-158

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Cite this article:
Liu H, Guo D, Sun F, et al. Embodied tactile perception and learning. Brain Science Advances, 2020, 6(2): 132-158. https://doi.org/10.26599/BSA.2020.9050012

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Received: 24 April 2020
Revised: 25 May 2020
Accepted: 29 May 2020
Published: 31 August 2020
© The authors 2020

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).