School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
Institute of Functional Nano and Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, Jiangsu, PR China.
Nat Commun. 2024 Aug 15;15(1):7056. doi: 10.1038/s41467-024-51403-9.
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.
模仿触觉感觉神经以实现具有人工智能的机器人的先进感觉功能引起了极大的关注。然而，此类设备仍然庞大，缺乏可靠的功能来进一步对具有本体感受反馈的突触设备进行功能化。在这里，我们报告了一种具有低工作偏置（-0.6 V）的人工有机传入神经，该神经通过集成压力激活的有机电化学突触晶体管和人工机械感受器来实现。为了感知物体的定向运动，实现了神经机器人的树突集成功能，进一步通过利用分布式和平行网络降低了控制的复杂性。组装有人工传入神经的智能机器人，再加上闭环反馈程序，可在发生物体滑动时迅速执行防滑识别和预防动作。经过使用深度学习模型对尖峰编码信号进行处理，触觉模式的时空特征得到了很好的区分，具有很高的识别精度。这项工作代表了模仿突触行为的突破，这对于下一代智能神经机器人和低功耗仿生电子学至关重要。
