Optical synapses have an ability to perceive and remember visual information, making them expected to provide more intelligent and efficient visual solutions for humans. As a new type of artificial visual sensory devices, photoelectric memristors can fully simulate synaptic performance and have great prospects in the development of biological vision. However, due to the urgent problems of nonlinear conductance and high-energy consumption, its further application in high-precision control scenarios and integration is hindered. In this work, we report an optoelectronic memristor with a structure of TiN/CeO₂/ZnO/ITO/Mica, which can achieve minimal energy consumption (187 pJ) at a single pulse (0.5 V, 5 ms). Under the stimulation of continuous pulses, linearity can be achieved up to 99.6%. In addition, the device has a variety of synaptic functions under the combined action of photoelectric, which can be used for advanced vision. By utilizing its typical long-term memory characteristics, we achieved image recognition and long-term memory in a 3 × 3 synaptic array and further achieved female facial feature extraction behavior with an activation rate of over 92%. Moreover, we also use the linear response characteristic of the device to design and implement the night meeting behavior of autonomous vehicles based on the hardware platform. This work highlights the potential of photoelectric memristors for advancing neuromorphic vision systems, offering a new direction for bionic eyes and visual automation technology.

Exoskeletons typically work by implementing motions programmed in advance and having the user call for them, making it difficult to use them for a wide range of motions in real-life environments. Now, in a notable example for wearable robotics, published in npj Robotics, researchers from the RIKEN Guardian Robot Project in Japan have used artificial intelligence to better assist users, by designing an exoskeleton that functions based on inputs regarding the user’s status as well as a view of the environment based on the user’s perspective.

Professor Daniel Seita collaborated with a student team in developing the MOTIF Hand, a tool advancing the capabilities of previous robot hand technology.

The University of Pennsylvania's Bo Zhen and postdoctoral researcher Li He developed a system for guiding light through tiny crystals in ways that allow it to navigate undeterred by bumps and defects. Their work could lead to sturdier lasers, faster data links, and light-based chips that don’t get tripped up by imperfections.