Recycling robotics company AMP Robotics raise to $70M


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AMP Robotics, the recycling robotics technology developer backed by investors including Sequoia Capital and Sidewalk Infrastructure Partners, is close to closing on as much as $70 million in new financing, according to multiple sources with knowledge of the company’s plans.

The new financing speaks to AMP Robotics’ continued success in pilot projects and with new partnerships that are exponentially expanding the company’s deployments.

Earlier this month the company announced a new deal that represented its largest purchase order for its trash sorting and recycling robots.

That order, for 24 machine learning-enabled robotic recycling systems with the waste handling company Waste Connections, was a showcase for the efficacy of the company’s recycling technology.

That comes on the back of a pilot program earlier in the year with one Toronto apartment complex, where the complex’s tenants were able to opt into a program that would share recycling habits monitored by AMP Robotics with the building’s renters in an effort to improve their recycling behavior.

The potential benefits of AMP Robotic’s  machine learning enabled robots are undeniable. The company’s technology can sort waste streams in ways that traditional systems never could and at a cost that’s far lower than most waste handling facilities.

As TechCrunch reported earlier the tech can tell the difference between high-density polyethylene and polyethylene terephthalate, low-density polyethylene, polypropylene, and polystyrene. The robots can also sort for color, clarity, opacity, and shapes like lids, tubs, clamshells, and cups — the robots can even identify the brands on packaging.

AMP’s robots already have been deployed in North America, Asia and Europe, with recent installations in Spain and across the U.S. in California, Colorado,  Florida, Minnesota, Michigan, New York, Texas, Virginia and Wisconsin.

At the beginning of the year, AMP Robotics  worked with its investor, Sidewalk Labs on a pilot program that provided residents of a single apartment building representing 250 units in Toronto with detailed information about their recycling habits. Sidewalk Labs is transporting the waste to a Canada Fibers material recovery facility where trash is sorted by both Canada Fibers employees and AMP Robotics.

Once the waste is categorized, sorted and recorded, Sidewalk communicates with residents of the building about how they’re doing in their recycling efforts.

It was only last November that the Denver-based AMP Robotics raised a $16 million round from Sequoia Capital  and others to finance the early commercialization of its technology.

As TechCrunch reported at the time, recycling businesses used to be able to rely on China to buy up any waste stream (no matter the quality of the material). However, about two years ago, China decided it would no longer serve as the world’s garbage dump and put strict standards in place for the kinds of raw materials it would be willing to receive from other countries.

The result has been higher costs at recycling facilities, which actually are now required to sort their garbage more effectively. At the time, unemployment rates put the squeeze on labor availability at facilities where trash was sorted. Over the past year, the COVID-19 pandemic has put even more pressure on those recycling and waste handling facilities, despite their identification as “essential workers”.

Given the economic reality, recyclers are turning to AMP’s technology — a combination of computer vision, machine learning and robotic automation to improve efficiencies at their facilities.

And, the power of AMP’s technology to identify waste products in a stream has other benefits, according to chief executive Matanya Horowitz.

“We can identify… whether it’s a Coke or Pepsi can or a Starbucks  cup,” Horowitz told TechCrunch last year. “So that people can help design their product for circularity… we’re building out our reporting capabilities and that, to them, is something that is of high interest.”


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