Expecting intelligent machines to efficiently work in real world requires a new method to understand unstructured information in unknown environments with good accuracy, scalability and generalization, like human. Here, a memristive neural computing based perceptual signal differential processing and learning method for intelligent machines is presented, via extracting main features of environmental information and applying associated encoded stimuli to memristors, we successfully obtain human-like ability in processing unstructured environmental information, such as amplification (>720%) and adaptation (<50%) of mechanical stimuli. The method also exhibits good scalability and generalization, validated in two typical applications of intelligent machines: object grasping and autonomous driving. In the former, a robot hand experimentally realizes safe and stable grasping, through learning unknown object features (e.g., sharp corner and smooth surface) with a single memristor in 1 ms. In the latter, the decision-making information of 10 unstructured environments in autonomous driving (e.g., overtaking cars, pedestrians) are accurately (94%) extracted with a 40x25 memristor array. By mimicking the intrinsic nature of human low-level perception mechanisms in electronic memristive neural circuits, the proposed method is adaptable to diverse sensing technologies, helping intelligent machines to generate smart high-level decisions in real world.

翻译：期待智能机器在现实世界中高效工作，需要一种新的方法来理解未知环境中的非结构化信息，并具备良好的准确性、可扩展性和泛化能力，如同人类一样。本文提出了一种基于忆阻神经计算的环境信息感知差分处理与学习方法，通过提取环境信息的主要特征并将关联编码刺激应用于忆阻器，我们成功实现了类似人类处理非结构化环境信息的能力，例如机械刺激的放大（>720%）和适应（<50%）。该方法还展现出良好的可扩展性和泛化能力，并在智能机器的两种典型应用中得到验证：物体抓取和自动驾驶。在前者中，机器人手通过单个忆阻器在1毫秒内学习未知物体特征（如尖角和光滑表面），实验实现了安全稳定的抓取。在后者中，自动驾驶中10种非结构化环境（如超车车辆、行人）的决策信息被一个40×25忆阻器阵列准确提取（准确率达94%）。通过模仿人类低级感知机制在电子忆阻神经电路中的内在本质，所提方法可适应多种传感技术，帮助智能机器在现实世界中生成高级智能决策。