PDF(Pubmed)
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.
摘要：
在具有人工智能的机器人技术中模拟触觉感觉神经以实现高级感觉功能非常有趣。然而,这样的设备仍然笨重，缺乏可靠的能力来使具有本体感受反馈的突触设备功能化。这里,我们报告了一种通过整合压力激活的有机电化学突触晶体管和人工机械感受器而获得的具有低工作偏置（-0.6V）的人工有机传入神经。神经机器人的树突状整合功能是为了感知物体的定向运动，通过利用分布式和并行网络进一步降低控制复杂性。用人工传入神经组装的智能机器人，结合闭环反馈程序，可以在发生物体打滑时快速实施打滑识别和预防措施。通过深度学习模型处理尖峰编码信号后，触觉模式的时空特征得到很好的区分，具有较高的识别精度。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产品至关重要。
