Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot
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.
Authors
- Jiansheng Jie (ORCID: https://orcid.org/0000-0002-2230-4289)
- Nripan Mathews (ORCID: https://orcid.org/0000-0001-5234-0822)
- Xihu Wu (ORCID: https://orcid.org/0000-0002-6171-0917)
- Kunqi Hou
- Wei Lin Leong (ORCID: https://orcid.org/0000-0002-1402-0083)
- Cindy G. Tang (ORCID: https://orcid.org/0000-0002-8799-0873)
- Shuai Chen (ORCID: https://orcid.org/0000-0002-8562-5784)
- Xiujuan Zhang (ORCID: https://orcid.org/0000-0002-3244-9449)
- Qiang He (ORCID: https://orcid.org/0000-0002-3555-1852)
- Zhongliang Zhou (ORCID: https://orcid.org/0000-0001-6455-2363)
- Zhiyi Gao
- Ting Li (ORCID: https://orcid.org/0000-0003-0048-2284)
Institutions
- Nanyang Technological University (SG)
- Chinese Academy of Sciences (CN)
- Soochow University (CN)
- Ningbo Institute of Industrial Technology (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1038/s41467-024-51403-9
- Citations
- 36
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 7.51
Funders
- National Research Foundation
- Agency for Science, Technology and Research
- National Research Foundation Singapore
- Ministry of Education - Singapore
- National Natural Science Foundation of China
- Ministry of Education, India
- Fundo para o Desenvolvimento das Ciências e da Tecnologia