Efficient operation of intelligent machines in the real world requires methods that allow them to understand and predict the uncertainties presented by the unstructured environments with good accuracy, scalability and generalization, similar to humans. Current methods rely on pretrained networks instead of continuously learning from the dynamic signal properties of working environments and suffer inherent limitations, such as data-hungry procedures, and limited generalization capabilities. Herein, we present a memristor-based differential neuromorphic computing, perceptual signal processing and learning method for intelligent machines. The main features of environmental information such as amplification (>720%) and adaptation (<50%) of mechanical stimuli encoded in memristors, are extracted to obtain human-like processing in unstructured environments. The developed method takes advantage of the intrinsic multi-state property of memristors and exhibits good scalability and generalization, as confirmed by validation in two different application scenarios: object grasping and autonomous driving. In the former, a robot hand experimentally realizes safe and stable grasping through fast learning (in ~1 ms) the unknown object features (e.g., sharp corner and smooth surface) with a single memristor. In the latter, the decision-making information of 10 unstructured environments in autonomous driving (e.g., overtaking cars, pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.

翻译：智能机器在真实世界中的高效运行需要具备类似人类的能力，能准确、可扩展且泛化地理解与预测非结构化环境中的不确定性。现有方法依赖预训练网络而非持续学习工作环境的动态信号特性，存在数据需求量大、泛化能力受限等固有限制。本文提出一种基于忆阻器的差分神经形态计算、感知信号处理与学习方法，用于智能机器。通过提取忆阻器中编码的机械刺激信号放大（>720%）与适应（<50%）等环境信息主要特征，实现非结构化环境下的类人处理。该方法利用忆阻器固有的多态特性，在两个不同应用场景（物体抓取与自动驾驶）的验证中展现出良好的可扩展性与泛化能力。在物体抓取场景中，机械手通过单个忆阻器快速学习（约1毫秒）未知物体特征（如尖锐棱角与光滑表面），实验实现了安全稳定的抓取。在自动驾驶场景中，采用40×25忆阻器阵列可准确（94%）提取10种非结构化环境（如超车、行人）的决策信息。通过模拟人类低级感知机制的内在本质，本文提出的电子忆阻神经形态电路方法展现出适应多样化传感技术、帮助智能机器在真实世界中生成智能高层决策的潜力。