More specifically, AI is the enabling technology essential for materials recovery facilities (MRFs) to generate and capture data to optimize recycling operations. Vision-based AI software identifies and characterizes objects in the waste stream in real time, digitizing each item that passes by. These objects are captured as a new form of data, including object counts, packaging descriptions, and more, over time. AMP has deployed hundreds of robots and sensors that process billions upon billions of objects on conveyor belts in MRFs. This effectively enables automated and continuous characterization of this material. The more AI-based robots and sensors deployed translates to an exponential increase in the fleet’s sorting intelligence.

When a challenging packaging type or new material emerges, AMP captures the imagery and trains its AI to identify the object. This knowledge is then deployed throughout the fleet of robots, expanding the AI’s material knowledge.

Recycling involves infinite variability in the kinds, shapes, and orientations of the objects on a conveyor belt, requiring nearly instantaneous identification along with the quick dispatch of a new trajectory to the robot arm used to sort the material on the belt. Training a neural network to detect objects in the recycling stream is not easy—but it’s an entirely different challenge when you consider the physical deformations that these objects can undergo by the time they reach a recycling facility. They can be folded, torn, smashed, or partially obscured by other objects.

AMP’s AI platform recognizes 67 billion objects (different containers and packaging types) on an annual basis; the number of items the company identifies on a monthly basis has steadily grown, from under 3 billion in January 2022 to approximately 4.5 billion in March to more than 7 billion in August. AI accuracy and performance is built on training a platform with as many real-world images as possible—and AMP has seen the most materials, in more permutations.