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：期望智能机器能够在真实世界中高效工作，需采用新方法实现对未知环境中非结构化信息的准确、可扩展且泛化性强的理解，如同人类一样。本文提出了一种基于忆阻神经计算的感知信号差分处理与学习方法，通过提取环境信息的主要特征，并将关联的编码刺激施加至忆阻器，成功实现了对非结构化环境信息的人类类似处理能力，例如机械刺激的放大（>720%）与适应（<50%）。该方法在智能机器两大典型应用——物体抓取与自动驾驶中验证了其良好的可扩展性与泛化性。前者通过单个忆阻器在1毫秒内学习未知物体特征（如尖锐棱角与光滑表面），实验实现了机器人手的安全稳定抓取；后者采用40×25忆阻器阵列，准确（94%）提取了自动驾驶中10种非结构化环境（如超车、行人等）的决策信息。通过模拟人类低级感知机制在电子忆阻神经电路中的内在本质，所提方法可适配多种传感技术，助力智能机器在真实世界中生成高级智能决策。