  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.
