Goldilocks of AI Robots is Light & Nimble

Refraction AI creator of the REV-1, a low-cost, lightweight autonomous delivery robot that can operate in both the bike lane and on the roadway,  launched at TechCrunch Mobility. Founded by University of Michigan professors Matthew Johnson-Roberson and Ram Vasudevan, the company claims it has developed a safer, more cost-effective solution for last mile logistics that an operate in both car and bike lanes. The company is backed by eLab Ventures and Trucks Venture Capital

“We have created the Goldilocks of autonomous vehicles in terms of size and shape,” said Matthew Johnson-Roberson, cofounder and CEO at Refraction. “Our platform is lightweight, nimble and fast enough to operate in the bike lane and on the roadway, and we are tackling regional inclement weather patterns that inhibit or slow down other AV solutions.”

Approximately the size of an electric bicycle, Refraction’s first self-driving delivery robot has three wheels and stands 5 feet tall, 4.5 feet long and 30 inches wide. It weighs approximately 100 pounds and can reach a speed of up to 15 mph, which is fast enough to be nimble and deliver in a timely manner, while having the shortest stopping distance of any AV on the road. The inside of the vehicle holds 16 cubic feet or approximately four or five grocery bags. When a delivery arrives at its destination, a text with a keypad code lets the recipient retrieve their goods. The company’s first test application is with restaurant partners, and the company expects to expand across the gamut of last mile delivery.

“Consumers today expect on-demand goods of every type, and timeliness of delivery is often the key to customer satisfaction. But companies are struggling to find consistent, reliable and economical ways to address that need,” said Bob Stefanski, Managing Director of eLab Ventures. “Refraction’s use of sturdy, smaller-sized delivery robots in the bike lane allows for faster technology development and covers a larger service area than competitors operating on the sidewalk. Their vehicles are also light-weight enough to deploy more safely than a self-driving car or large robot. The market is huge, especially in densely populated areas.”

The REV-1 uses a system of 12 cameras as its primary sensor system, along with radar and ultrasound sensors for additional safety. The entire platform costs a fraction of just one LIDAR used in other systems. Additionally, this system enables Refraction’s platform to navigate in rain or snow, and is not dependent on traditional HD LIDAR maps.

“Our vehicle’s low curb weight at low speeds makes deployment safer than other autonomous vehicles. For example, we have a 5 foot stopping distance, compared to the typical 45 foot stopping distance that a full-sized vehicle at the same speed would need to avoid an accident,” continued Johnson-Roberson. “Finally, our design and technical choices, particularly relying on cameras over HD-LIDAR, allow us to operate a more economical platform that gives us a significant competitive advantage on cost efficiency.”