Robotics AI Startup Covariant Lifts $40M To Brings Robots To Warehouses

California-based AI-powered robotics startup, Covariant, has raised $40 million in Series B funding round led by San Francisco-based venture capital firm, Index Ventures. The startup is planning to ramp up its hiring and to expand its robotic control systems for new industries to create an advanced system suitable for warehouses.

AI-focused Radical Ventures as a new investor participated in the funding along with the existing investors, including Amplify Partners. This round brings Covariant’s total funding to $67 million.

The startup claims to help industries who are looking for robotics and automation in their warehouses as a potential solution forward amid the coronavirus pandemic. As the startup manufactures AI software that helps warehouse robots to pick, place, and unload objects more quickly than human workers, with a rough accuracy rate of 95%. 

Pieter Abbeel and Peter Chen founded Covariant in 2017. However, the startup came out of the stealth mode in January 2020 with the announcement that it had already employed its real-world facilitating technology in North America and Europe. Just after 2 months, in March, the startup announced its partnership with ABB, the leading industrial robotics company.

Presently, the startup is engaged with Knapp AG, Austria’s logistics-automation company, and ABB Ltd, a Swedish multinational corporation, which provides various hardware parts such as robot arms or conveyor belts to the startup.

In an interview, Covariant co-founder Peter Chen said, we are building a universal brain for robotic manipulation tasks by providing the software, and ABB is providing the rest of the system.  

Covariant is using its camera systems for capturing images of the object, and it is using AI to analyze the object and self-guide on how to pick the items. The embedded ML technology widely helps Covariant-powered robots to learn from experience.

In a press release, Covariant co-founder Pieter Abbeel said, while founding Covariant, our ultimate goal was to make AI Robotics capable of working autonomously in the real world. We have reached that milestone now, and we see a considerable benefit in widening our universal AI to new customer environments, use cases, and industries.