They are coming—for your trash. Sorting through 67 million tons of glass, plastic and paper is dirty, low-paid, mind-numbing work. Matanya Horowitz’s AMP Robotics wants to take humans off the job.
At an RDS of Virginia recycling center in Roanoke, two spider-like, 300-pound robots sort through an unending line of trash. One robot’s skinny leg, which relies on computer vision to detect recyclables, plucks a hunk of blue plastic off a conveyor belt, while the other’s grabs a piece of an old water bottle. The machine then places those bits into sorting bins using a vacuum gripper.
For the nation’s 600-plus recycling facilities, which process some 67 million tons of waste, these leggy robots from AMP Robotics are one answer to the current bottlenecks facing the industry. Even before Covid-19 struck, AMP Robotics was starting to gain traction. But as boxes from home deliveries piled up at recycling centers and hiring—already a tough proposition—got even tougher as workers feared getting ill, AMP’s business boomed. “It’s repetitive and not ergonomic, and you are surrounded by unsanitary stuff like hypodermic needles,” says AMP founder and CEO Matanya Horowitz. “With Covid on top of that, people are saying, ‘Do I really want to put my hands in this material that maybe came from an infected person’s house?’”
AMP, which is based in Louisville, Colorado, has sold or leased 100 of its AI-powered robots since 2017 to more than 40 recycling facilities in North America, Europe and Japan. They’re not cheap, at a cost of up to $300,000 (or around $6,000 a month to lease), but those recycling centers are betting that the hefty capital expense will pay off with lower employment costs and higher efficiency. Forbes estimates that AMP’s revenue this year will reach $20 million, double its $10 million for 2019. And there’s lots of room for growth: Recycling is a $6.2 billion (revenue) market in the U.S., and while the overall market has been growing at less than 2% a year (and declined this year due to Covid-19), facilities are trying to figure out how to get more out of their waste, the majority of which still ends up in landfills.
Thanks to both its technological promise and fast growth, AMP was featured on both Forbes’ AI 50 list of artificial intelligence companies to watch and the Survivors and Thrivers list of 25 small business standouts that outperformed during the pandemic. While Horowitz, who has a Ph.D. in robotics from Caltech, bootstrapped the business to start, AMP has now raised $23 million in venture funding. Forbes estimates it reached a $100 million valuation with its latest round, in November 2019, led by top VC firm Sequoia.
“In my opinion, Matanya is one of the 50 best roboticists in the world,” says Sequoia partner Shaun Maguire, a classmate of Horowitz’s who led the funding round.
A bearded and balding man who is just 33, Horowitz speaks with childlike wonder when talking about automation. He fell in love with robots watching Transformers and Voltron cartoons growing up in Boulder, Colorado. His father, Isaac, an engineering professor at the University of Colorado, was renowned in the field for his work on control theory, a concept based on the math behind planning and reacting to one’s environment that was used at the time for fighter aircrafts and chemical plants. The elder Horowitz (who died in 2005) did not talk about his work at home, but years later, his son received a Ph.D. in the same subject because he thought the underlying math would be necessary for robots and artificial intelligence. “It might’ve been genetic,” Matanya says.
Horowitz fast-tracked his education with college-level classes during middle school, and completed both bachelor’s and master’s degrees in electrical engineering from the University of Colorado Boulder (along with three other diplomas in computer science, applied mathematics and economics) in four years. He spent time in a research lab hacking Roombas to perform cooperative activities, like moving a chair together. After college, he moved to California, completing a Ph.D. focused on robotic path planning at Caltech.
While there, Horowitz was captivated by the emerging technology of deep learning, which can allow robots to see things in the same ways as humans through computer vision. While other young roboticists pursued flashier technology such as self-driving cars or autonomous drones, Horowitz noticed the drab field of recycling. He realized that the industry, slow to adopt new technologies, presented a ripe opportunity to use technology on the cutting edge without having to compete with Google or Lockheed Martin. He was also drawn to using robotics to decrease the amount of waste that isn’t recycled, and improve the environment.
To learn about recycling, Horowitz spent his weekends driving to recycling facilities around Los Angeles. During these jaunts, he was struck by how much labor was required to sort through the junk—and how unpleasant it was. “There’s a pile of garbage with diapers sitting on top, and somebody’s trying to drag a garden hose out of this thing and stuff is landing on them,” he recalls. That pain, he figured, made it an industry waiting to be brought into the modern era with automation.
In the fall of 2014, Horowitz quit his postdoctoral research at Caltech and moved back to his native Colorado to start AMP Robotics, named for its technological aim of “autonomous manipulation and perception.” By then, recycling robots had “occurred to pretty much everybody,” Horowitz says. But the idea could only become reality after faster computer processing speeds opened up the potential of deep learning to transform existing automotive manufacturing robots for a range of new conveyor belt-based use cases.
Recycling robots were further along in Europe than in the United States. Appingedam, Netherlands-based Bollegraaf, which had filed patents for recycling robots in the 1990s, for example, had created robots that rely on spectroscopy and the heights of objects to sort waste. Meanwhile, Helsinki, Finland-based ZenRobotics had ones that could sort construction and demolition materials with color-coding cameras, laser sensors and metal detectors based on neurorobotics research. Horowitz took a similar approach, but focused on single-stream recycling, where newspaper, cardboard and plastics are all mixed together, as the area to apply deep learning that could teach the robots how to recognize objects based on colors, shapes, textures and logos.