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Daily news and analysis about the food tech revolution

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The Food Robot Roundup: Zomato Invests in Mukunda, Ramen on Wheels

The last few weeks have been pretty eventful in the food robotics space. Here’s our latest food robot roundup to catch you up on some of the most interesting stories.

 Strio.AI Says Goodbye to Agtech With Zoox Acquihire

Strio.AI, a Boston-based robotics company founded by MIT alumnus in 2020, was acquired by Zoox, the robotaxi firm owned by Amazon. Strio.AI automates the picking and pruning strawberry crops and has been tested on farms in California and Florida.  

Automating fruit picking is challenging, which is why Strio.AI’s fast pace of testing is impressive. However, Zoox is looking to utilize Strio.AI’s expertise to bulk up its computer vision team, which means the Strio.AI team will be winding down its agtech business. The Strio team will lead Zoox’s Perception product, the computer vision software that drives Zoox’s automation. 

The Strio.AI deal is just the latest in a string of agtech automation acquisitions. Last month, strawberry-picking robot Traptic was acquired by Bowery, a New York based vertical farm, and pivoted from outdoor to indoor farming. Traptic was founded in 2016 and claims to pick 100,000 strawberries a day, preserving the fruit by pulling by the stem and not touching the strawberry directly. The technology will be adapted and integrated into Bowery’s existing hardware and software since the original tractor-like system isn’t conducive in a vertical farming environment. In April 2021, Root.ai, a company with a machine that picks grape tomatoes with a three-pronged robotic gripper, was acquired by AppHarvest, the operator of the largest greenhouse in the U.S. and now uses four- and eight-fingered grippers to pick strawberries and cucumbers. Since acquisition, picking rates have doubled and the next step is to reduce the costs of the robot.

NVIDIA Invests in Serve Robotics

NVIDIA is investing $10 million in Serve Robotics to expand its sidewalk robot delivery service outside of Los Angeles and San Francisco. While NVIDIA is a new investor in the robotic delivery space, the two companies are familiar with one another as Serve utilizes NVIDIA’s synthetic data generation tools for training and testing Serve’s models in simulations as well as robotic fleet management. 

Serve is a fully automated and fully self-driving last-mile delivery service and the startup raised $13 million in an expanded seed round in December. Last-mile delivery services, both on the ground and in the sky have seen significant growth in recent years as the pandemic increased delivery orders. As the sidewalks and streets get more crowded, here’s a table to help you understand the little robots you might be sharing the roads within the near future. 

Zomato Invests in Mukunda

Indian multinational food delivery company Zomato has acquired a 16.66% stake in Mukunda Foods, a food robotics company that designs and manufactures smart robotic equipment for restaurant automation. The stake comes with a price tag of $5 million. The deal puts Mukunda at $30 million valuation.

Mukunda Foods offers end-to-end kitchen automation solutions for QSR and Cloud brands and their six products have been installed in over 2000 locations. Their unique proposition is Nucleus, kitchen automation as a service (KAAS), which enables brands to expand cloud kitchens to new locations with a fully operational kitchen equipped with automated equipment, no rentals on property, no operational concerns, high consistency of products, and highly scalable profits. They’ve already partnered with several brands in Bangalore and plan to expand to other cities in India. 

There are a lot of opportunities for synergy since Mukunda Foods serves ghost kitchens and the ghost kitchens on Zomato have carved niches with loyal customer bases. There’s also a significant market opportunity since the Indian cloud kitchen market is projected to be between $2 and $2.8 billion USD by 2025. 

Ramen on wheels

In a commercial that debuted this month, Nissan showcased the e-4ORCE technology on its new Ariya by putting it on a self-driving car that delivers hot bowls of ramen to customers. The technology is designed to reduce abruptness and swaying for passenger comfort which is great for making sure that ramen soup doesn’t spill. On the Ramen Counter, the soup bowl sits on a flat tray and twin electric motors independently control the front and rear wheels. 

Unfortunately, it looks like this is more of a concept for advertising the Nissan Ariya and Nissan probably won’t make this available commercially but it’s cool to imagine a fresh bowl of ramen zooming down the counter to you. You can watch the video below.

 

There Should be an Open Brand Image Database to Train Robots and Cashierless Checkout Systems

It was a box of Cheez-Its that made me think of the idea.

We were recently at Nvidia’s Robotics Seattle Robotics Lab, watching a presentation on how the company uses computer vision and synthetic data to train robots in the kitchen. In order for a robot to grab a box of Cheez-Its, that robot needs to know what a Cheez-Its box looks like. In order to teach the robot what a Cheez-Its box looks like, Nvidia needs give the robot detailed information about that box including size, shape, and artwork on the front.

This isn’t that hard if your kitchen, like the one at Nvidia, is stocked with only one box of Cheez-Its. You scan that box, input the dimensions and imagery so the robot can match what you’ve scanned to the real thing. But what happens when when you want the robot to find a box of something other than Cheez-Its? Or if the pantry has many different types of Cheez-Its, that kinda look similar but have different flavors? Or if it’s the holidays and the box has been altered from that first model so it now has a snowman on it?

Being able to identify particular products via computer vision isn’t just an issue for robotic hands. Startups like Grabango and Trigo Vision are retro-fitting grocery stores with lots of tiny cameras that use computer vision for cashierless checkout. These cameras need to precisely recognize the items that shoppers pick up and so that the consumer can be accurately charged. That means the AI powering the system needs to know the differences between a bottle of Coke, Diet Coke and Coke Zero, and be able to understand any changes to branding, like a new logo or seasonal updates.

Rather than having each robotics company and every cashierless checkout company separately create their own database of product images, it seems like having some sort of central repository of brand images would be useful. Think of it as a giant library of constantly updating brand images for all the products in a grocery store. CPG companies would upload 3D models of the latest versions of their products to this database, giving computer vision companies access the most up-to-date imagery for training their respective applications.

This is definitely not the most pressing issue facing CPG companies or retailers; cashierless checkout and product picking robots are still very much in the early stages. But they are coming — and preparing for their arrival now would make the evolution of computer vision and robotics that much faster. After all, training those systems is much easier when you can just download an image rather than creating it yourself.

During our visit I asked Dieter Fox, Senior Director of Robotics Research at NVIDIA, if there was such a system. He said there was for common objects, but not brand specific. ShapeNet has a 3D database of 50,000 common objects, and its subset, PartNet recently launched with a database of more than 26,000 objects broken down into their various parts.

There are competitive issues that might have CPG brands balking at the idea. Coke may not want people knowing about a particular branding change or partnership in advance. But the overall concept could be a tide that lifts all boats. It gives computer vision-related companies the most accurate 3D models of products for training purposes. The faster computer vision systems can be trained, the faster they can work in the real world without any hiccups, which would ideally allow brands to sell more products. It would also make it easier for kitchen robots, when they eventually arrive, to autonomously grab ingredients needed while cooking (“Robot, grab the turmeric.”).

This isn’t just for food, obviously. This type of repository could work for any brand across any sector that will involve computer vision. Perhaps it’s something Dieter Fox can talk about when he speaks at our upcoming Smart Kitchen Summit in October. Get your ticket now and maybe you can talk with him about it over a box of Cheez-Its.