Scientists have developed a new artificial eye inspired by the adaptive vision of animals and popularized in science fiction films like Terminator. The technology uses a liquid-metal pupil that automatically changes shape and size in response to light, potentially helping robots, autonomous vehicles, and advanced machines see more clearly in rapidly changing environments.
Researchers from the University of North Carolina at Chapel Hill, Westlake University, and other institutions introduced the concept in a study published in the journal Science Robotics. Their goal was to address a common challenge in modern machine vision systems: cameras and sensors often struggle when lighting conditions change suddenly, such as moving from darkness into bright sunlight.
Unlike biological eyes, many computer-vision systems rely heavily on software processing to compensate for overexposure or low light
These methods can be slow, energy-intensive, and sometimes unreliable. The new system instead takes inspiration directly from nature by replicating the pupillary light reflex, the automatic process that allows human and animal pupils to adjust instantly to changing light levels.
At the center of the technology is a liquid-metal pupil made from eutectic gallium-indium (EGaIn). This material is embedded within flexible microchannels and controlled through electrochemical signals. When bright light hits the artificial retina, it generates electrical pulses that trigger the liquid metal to contract, reducing the amount of light entering the system. When the environment becomes darker, the pupil expands again to capture more light.
The researchers also designed the system so the pupil can change shape, not just size. In addition to circular pupils like those found in humans, the device can replicate shapes seen in animals such as cats, frogs, sheep, or squids, which can help adapt vision systems to different environments.
The artificial eye consists of three key components
First is a hemispherical artificial retina made of light-sensitive photodetectors arranged in a curved structure. Second are liquid-metal “neurons” that convert light signals into electrical pulses. Third is the adaptive liquid-metal pupil that adjusts the aperture based on those signals. Together, these elements create a closed-loop system that mimics how biological eyes regulate light exposure.
Early tests suggest the approach could significantly improve machine vision. In one experiment, image recognition accuracy in harsh lighting increased from about 68 percent to more than 83 percent when the adaptive pupil system was activated.
This improvement matters because vision is one of the most critical capabilities for emerging technologies such as robots, drones, and self-driving cars. These systems must operate in unpredictable real-world conditions where lighting can change quickly – from dark tunnels to bright daylight, for example.
A hardware-based solution like the liquid-metal pupil could reduce the need for complex image-processing algorithms while improving speed and energy efficiency. That makes the technology particularly promising for mobile systems where power consumption and processing speed are critical.
The potential applications go beyond robotics and autonomous vehicles
Researchers say the technology could also improve security cameras, medical imaging devices, drones, and neuromorphic computing systems that attempt to replicate biological brain functions.
For now, the artificial eye is still a proof-of-concept prototype, but the team is already working on refining the design. Future work will focus on miniaturizing the liquid-metal actuators and photodetectors, improving energy efficiency, and integrating the system into real-world devices.
Researchers also plan to expand the system with additional sensing capabilities, including color and multispectral imaging, and potentially combine it with tactile or motion sensors to create machines with more comprehensive perception.
If those developments succeed, the liquid-metal pupil could represent an important step toward machines that see the world more like humans – and animals – do, allowing robots and vehicles to navigate complex environments with far greater awareness.
