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The cognition of spatiotemporal tactile stimuli, including fine spatial stimuli and static/dynamic temporal stimuli, is paramount for intelligent robots to feel their surroundings and complete manipulation tasks. However, current tactile sensors have restrictions on simultaneously demonstrating high sensitivity and performing selective responses to static/dynamic stimuli, making it a challenge to effectively cognize spatiotemporal tactile stimuli. Here, we report a high-sensitive and self-selective humanoid mechanoreceptor (HMR) that can precisely respond to spatiotemporal tactile stimuli. The HMR with PDMS/chitosan@CNTs (PDMS: polydimethylsiloxane; CNT: carbon nanotube) graded microstructures and polyurethane hierarchical porous spacer exhibits high sensitivity of 3790.8 kPa−1. The HMR demonstrates self-selective responses to static and dynamic stimuli with mono signal through the hybrid of piezoresistive and triboelectric mechanisms. Consequently, it can respond to spatiotemporal tactile stimuli and generate distinguishable and multi-type characteristic signals. With the assistance of the convolutional neural network, multiple target objects can be easily identified with a high accuracy of 99.1%. This work shows great potential in object precise identification and dexterous manipulation, which is the basis of intelligent robots and natural human-machine interactions.


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A high-sensitive and self-selective humanoid mechanoreceptor for spatiotemporal tactile stimuli cognition

Show Author's information Shuxin Bi1,2,§Xuan Zhao1,2,§Fangfang Gao1,2Xiaochen Xun1,2Bin Zhao1,2Liangxu Xu1,2Tian Ouyang1,2Qingliang Liao1,2( )Yue Zhang1,2( )
Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology (Beijing), Beijing 100083, China
Key Laboratory of Advanced Materials and Devices for Post-Moore Chips, Ministry of Education, Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China

§ Shuxin Bi and Xuan Zhao contributed equally to this work.

Abstract

The cognition of spatiotemporal tactile stimuli, including fine spatial stimuli and static/dynamic temporal stimuli, is paramount for intelligent robots to feel their surroundings and complete manipulation tasks. However, current tactile sensors have restrictions on simultaneously demonstrating high sensitivity and performing selective responses to static/dynamic stimuli, making it a challenge to effectively cognize spatiotemporal tactile stimuli. Here, we report a high-sensitive and self-selective humanoid mechanoreceptor (HMR) that can precisely respond to spatiotemporal tactile stimuli. The HMR with PDMS/chitosan@CNTs (PDMS: polydimethylsiloxane; CNT: carbon nanotube) graded microstructures and polyurethane hierarchical porous spacer exhibits high sensitivity of 3790.8 kPa−1. The HMR demonstrates self-selective responses to static and dynamic stimuli with mono signal through the hybrid of piezoresistive and triboelectric mechanisms. Consequently, it can respond to spatiotemporal tactile stimuli and generate distinguishable and multi-type characteristic signals. With the assistance of the convolutional neural network, multiple target objects can be easily identified with a high accuracy of 99.1%. This work shows great potential in object precise identification and dexterous manipulation, which is the basis of intelligent robots and natural human-machine interactions.

Keywords: sensors, mechanoreceptors, self-selective, high-sensitive, spatiotemporal tactile stimuli cognition

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Publication history
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Acknowledgements

Publication history

Received: 13 July 2023
Revised: 12 September 2023
Accepted: 05 October 2023
Published: 09 November 2023
Issue date: May 2024

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2018YFA0703500), the National Natural Science Foundation of China (Nos. 52232006, 52188101, 52102153, 52072029, 51991340, and 51991342), the Overseas Expertise Introduction Projects for Discipline Innovation (No. B14003), the China Postdoctoral Science Foundation (No. 2021M700379), and the Fundamental Research Funds for Central Universities (No. FRF-TP-18-001C1).

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