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.

通过提取环境信息的主要特征并将相关编码刺激应用于忆阻器，我们成功地在处理非结构化环境信息方面获得了类人的能力，比如机械刺激的放大（>720%）和适应性（<50%）。这种方法还在智能机器的两个典型应用（物体抓取和自动驾驶）中展示了良好的可扩展性和泛化性能。