They did so using depth camera images of opaque objects paired with color images of those same objects. Once trained, the color camera system was applied to transparent and shiny objects.

But a color camera can see transparent and reflective objects as well as opaque ones. So CMU scientists developed a color camera system to recognize shapes based on color.

Researchers from Carnegie Mellon University used machine learning and color cameras to train robots to grab clear and shiny objects.

While this system works well enough on relatively dull opaque objects, it has problems with transparent items that much of the light passes through, or shiny objects that scatter the reflected light.

The transmission of light from the torch through opaque materials lets no light through. Translucent materials let small amounts of light through, and transparent materials allows all light to ...

Techniques for optical acquisition of physical objects and their surroundings lie at the heart of virtual reality (VR) systems and material design applications, but the digitization process becomes ...

When the light emanating from a colourful image passes through a translucent scattering object, the paths of its constituent photons change in complex, unpredictable ways. Once the light emerges, it ...

Transparent and reflective objects are the things of robot nightmares. Roboticists now report success with a new technique they've developed for teaching robots to pick up these troublesome objects.