Giving Robots the Ability to Understand What They Touch

Today's robotic systems struggle with a fundamental limitation: they can see shapes but not materials. AI Vision knows what things look like, but not what they are.

Matter's ultraspectral stack solves this. By capturing molecular signatures invisible to conventional cameras, our technology enables robots to identify materials with chemical precision - distinguishing between visually identical objects based on their actual composition. A robotic arm can determine if a surface is safe to grip, whether food is contaminated, or if a component is the correct alloy, all from vision alone.

This capability unlocks entirely new classes of automation. In manufacturing, robots can perform quality control that currently requires laboratory analysis. In logistics, they can safely handle unknown objects without risking damage. In agriculture, they can assess crop health and sort produce with unprecedented accuracy. In hazardous environments, they can identify materials without physical contact.

The implications extend beyond individual robots. Matter's Large World Model, trained on ultraspectral data, creates a universal perception layer that transfers across applications. A model trained to identify aerospace materials can adapt to recognize pharmaceutical compounds. One trained on agricultural data can help with recycling automation. This is vision infrastructure for an autonomous future - where machines don't just manipulate the world, but truly understand it.