When asked what kind of company it is, the team at Kiwi Campus always says that it is a delivery company, not a robotics firm — even though in these early days, its delivery robots are probably the most recognizable thing about the company.
But, according to co-founder and CTO Jason Oviedo, when he and co-founder and CEO Felipe Chavez Cortes were first thinking about founding a firm, robotics wasn’t on their minds. Building a delivery service — for students, by students — was.
However, Oviedo told PYMNTS in a recent interview, it didn’t take them very long to figure out that pure play delivery wasn’t a market where they could make a lot of progress, and it wasn’t until they started revising that initial idea that they started thinking about automation — and ultimately delivery robots — as their best entry point for the market.
“That makes a huge different because our entire process and design isn’t centered on building the flashiest robot — it is about how to use a robot to make the delivery process faster, cheaper and better.”
According to Oviedo, that helps Kiwi stay out of the over-design weeds that robotics startups sometimes wander into. It is easy to get so involved and invested in the tech that they end up building a perfectly specialized robot that is too expensive, too delicate or too likely to only work in very specific use cases because the machine gets built for precision instead of flexibility.
“To make the product affordable enough for use at scale, our bots have to be something we can produce cheaply. Our main sensor is a camera — we don’t have any other expensive hardware built on, there’s no LiDAR,” Oviedo noted.
Instead, he continued, the Kiwi team has focused on building up a lot of technology on the software side capable of “working with the huge amount of information is available in raw camera data” and turning it toward navigation.
“It’s not easy, but it’s doable,” Oviedo said.
And doable, he added, is always the firm’s focus. So when designing the robot-assisted automated delivery process, for example, the team didn’t try to build a single bot that could do all the heavy lifting from restaurant counter to consumer front doorstep.
Instead, he noted, it created a multi-modal system designed around three robots. The first works inside the restaurant, and is designed to solve the “first ten meters problem” of getting the goods — so far, food — out the door.
From there, the order goes to the semi-autonomous vehicle that delivers a batch of last mile robots, now loaded with their goods to deliver.
Once at the central point, the small bots on wheels solve the “last 400 meters” problem by rolling along to their recipients.
The service is a very early startup — and so far has delivered about 10,000 meals on automated wheels to about 10,000 people on the Berkeley Campus.
The response so far, Oviedo noted, has been overwhelmingly positive — customers like the novelty of getting their food delivered by bots, and like paying less for anything, always.
“At the end of the day, what we can do is impact the cost of delivery. So today if you are paying $6 or $7 per order, and then we tell them that because of automation they can pay a fraction of that? People like that idea a lot.”
And because their bots are designed to be small and compact — and to roll across pedestrian walkways only in short bursts — they have so far had mostly pleasant dealings with municipalities.
That is not always a guaranteed outcome when one is proposing to set delivery bots loose on a city’s sidewalks.
But so far, the Kiwi bots have not seen the world outside the Berkeley campus, where the incubator that houses their parent company is located.
The goal, according to Oviedo, is to change that as soon as possible.
“Right now, our two main priorities are to improve our delivery technology and to really work on building out scale. That means filling more orders, finding new places where our technology can work and testing new markets.”
It won’t be a short journey, and Kiwi is still starting out. But, according to its CTO, they’ve gotten very good at solving problems — 400 meters at a time.