Home How AMP Robotics Uses Automation and AI to Sort Materials within Waste Streams

How AMP Robotics Uses Automation and AI to Sort Materials within Waste Streams

by Ant Sh
How AMP Robotics Uses Automation and AI to Sort Materials within Waste Streams

Founded in 2015, AMP Robotics is reimagining and actively modernizing the world’s recycling infrastructure by applying AI and robotics to economically recover commodities reclaimed as raw materials for the global supply chain.

The company’s proprietary AI technology, AMP Neuron, works by looking at images of recyclable materials on conveyor belts within recycling facilities. The camera perceives material very similarly to the way a human would. Looking for specific colors, shapes, textures, logos, and more, the system recognizes patterns correlated with material type. Neuron digitizes these images and uses the data generated to infer in real time the recyclable materials and contaminants in sortation environments.

It continuously trains itself by processing millions of material images into data, building upon an ever-expanding neural network that adapts to changes in a facility’s material stream.

Neuron digitizes every image of each item it sees on the conveyor belt, then guides the robotic arm to pick the programmed material(s), according to customers’ settings. As more robots are deployed, the industry can leverage the networked intelligence of hundreds of units. The more AI-based robots and sensors are deployed into production, the more a network effect is created. This network effect exponentially increases the sorting intelligence.

If a challenging packaging type or new material emerges, AMP Robotics’ team is able to capture imagery and train the AI to identify the object. This knowledge is then deployed throughout the fleet of robots. What’s interesting is that the AI can learn to identify nearly anything a person can be taught to identify. This means AI can go beyond plastic resins or other material types. It can identify brands, form factors, certain types of damage. This gives a whole new level of sorting capabilities. For instance, it can identify aluminum foil versus aluminum cans, or food grade versus non-food grade polypropylene.

“Our mission is to apply technology to enable a world without waste. We’re already growing in areas like C&D [Construction and Demolition], e-scrap, and organics, but our goal is to apply our technology in any setting where we can boost the margin that can be made per ton of material. As the industry responds to the commitments made by consumer-packaged goods companies to use more post-consumer recycled content, the demand for AI and robotics to modernize existing recycling facilities continues to thrive. With these retrofits, we see opportunity in several areas, from the breadth and precision of material characterization capabilities, to increasing use of data to improve recycling operations, to helping policymakers achieve sustainability targets.”

Matanya Horowitz, CEO, AMP Robotics

*In the image above, AMP’s proprietary AI technology applies computer vision and deep learning to guide high-speed robotics systems to precisely identify and differentiate recyclables found in the waste stream by color, size, shape, opacity, consumer brand, and more, storing data about each item it perceives.

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