Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot

Shuai Chen, Zhongliang Zhou, Kunqi Hou, Xihu Wu, Qiang He, Cindy G. Tang, Ting Li, Xiujuan Zhang, Jiansheng Jie, Zhiyi Gao, Nripan Mathews () and Wei Lin Leong ()
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Shuai Chen: Nanyang Technological University
Zhongliang Zhou: Nanyang Technological University
Kunqi Hou: Nanyang Technological University
Xihu Wu: Nanyang Technological University
Qiang He: Nanyang Technological University
Cindy G. Tang: Nanyang Technological University
Ting Li: Nanyang Technological University
Xiujuan Zhang: Soochow University
Jiansheng Jie: Soochow University
Zhiyi Gao: Chinese Academy of Sciences
Nripan Mathews: Nanyang Technological University
Wei Lin Leong: Nanyang Technological University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, such devices remain bulky and lack reliable competence to functionalize further synaptic devices with proprioceptive feedback. Here, we report an artificial organic afferent nerve with low operating bias (−0.6 V) achieved by integrating a pressure-activated organic electrochemical synaptic transistor and artificial mechanoreceptors. The dendritic integration function for neurorobotics is achieved to perceive directional movement of object, further reducing the control complexity by exploiting the distributed and parallel networks. An intelligent robot assembled with artificial afferent nerve, coupled with a closed-loop feedback program is demonstrated to rapidly implement slip recognition and prevention actions upon occurrence of object slippage. The spatiotemporal features of tactile patterns are well differentiated with a high recognition accuracy after processing spike-encoded signals with deep learning model. This work represents a breakthrough in mimicking synaptic behaviors, which is essential for next-generation intelligent neurorobotics and low-power biomimetic electronics.

Date: 2024
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DOI: 10.1038/s41467-024-51403-9

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