Six months ago, Gao left his role at Instacart and Caper to tackle a new challenge—this time in robotics. His new company, Dyna Robotics, emerged from stealth on Tuesday with a $23.5 million seed round, co-led by CRV and First Round Capital, to build more affordable, easy-to-deploy AI-powered robots for brick-and-mortar businesses. The robots are intended to handle tasks ranging from dangerous to dull and dirty, including chopping food, loading dishes, folding laundry and cleaning toilets.

Gao founded Dyna Robotics, which he said is being valued at around $100 million in the latest funding round, with one of his Caper co-founders, engineer York Yang, as well as Jason Ma, a Google DeepMind alum. Ma was the lead author on Eureka, a widely-read paper on training robots with human-like dexterity.

Robots that are single task experts

With Caper, Gao said he helped grocery chains like ShopRite, Kroger and Aldi, as well as independent grocers, grow their businesses. Now, with his robotics startup, he hopes to do the same with a new set of customers: restaurants, groceries and dry cleaner shops.

Most companies in the “physical AI” space—that is, AI for real-world autonomous systems like robots and self-driving cars—are either working on general purpose AI models (such as Physical Intelligence and Skild) or humanoid robot hardware (like Figure AI and Agility Robotics). Dyna Robotics, however, is going a different route, building simple hardware in the form of a pair of stationary robotic arms, powered by an AI model trained to do one specific task or set of tasks. Gao said as far as he is aware, Dyna is the only non-humanoid robot company trying to put robot AI models fine-tuned on specific datasets into production. 

This narrow focus keeps costs down. Robots from some of the world’s most highly-valued robotic startups cost hundreds of thousands of dollars, if they’re even available at all. Dyna’s are expected to cost tens of thousands of dollars when they’re on sale. There are no firm dates yet for when the robots will debut, but Gao said it will likely be in the next few months. His robots are currently in trial production “but not fully live yet,” he said.

The goal is to automate many tasks that many people don’t want to do. “That’s a very, very high value for businesses of all kinds,” he added, especially since robots for many of these kinds of tasks don’t exist. For example, traditional machine learning struggles with the unpredictable nature of jobs like folding cloth, he explained. But today’s AI models can be trained to handle it—especially as Dyna Robotics focuses on collecting extensive data for specific tasks, rather than amassing vast and costly real-world data across a wide range of actions.

General-purpose robots will take a while

That is where Ma’s Eureka research comes in. While the tasks he explored in the paper—teaching a robot hand to spin a pen or a robot dog to juggle on a yoga ball—are not super-practical, Gao said the two bonded on the same idea: Creating an expert-level AI model for robots that can go into production very quickly. “I think he shares a very similar sentiment as me with regards to robotics, which is that getting to general-purpose robots is not going to happen as quickly as the industry hoped,” he said. Gao, Yang, and Ma wanted to shorten the timeline for making robots useful with Dyna’s robots that master one task at a time, which lets its AI models learn and improve in production environments. However, they are still working towards an ultimate goal of developing general-purpose AI-powered robots. 

The robotics industry, of course, is only getting more crowded: As of March 2024, there were reportedly over 1,500 robotics startups globally. And for many, convincing small to medium-sized business customers that robots are a better investment than humans may remain a tough sell.

However, Gao reiterated that few companies are currently able to scale their work quickly into production, as Dyna Robotics plans to do. In addition, there is a labor shortage in the types of jobs Dyna Robotics is tackling, such as food preparation, so he said convincing customers of the need is not difficult.

The biggest challenge, he said, is to get the robot AI models to work reliably and efficiently in a real-world production environment. “Right now the speed of foundation models is around 10-30% of human-level efficiency, and we are doing a ton of research to get us closer to human-level speeds,” he said. Dyna thinks it can achieve much better efficiency by focusing on one task at a time. 

Gao said the company, based in Redwood City, Calif., in the heart of Silicon Valley, already has 30 employees. As a second-time founder, he said he knows how to build products faster than before. “We have a very core philosophy that good engineering is still going to ultimately win,” he said. 

Still, starting Dyna Robotics is much harder than his Caper experience, Gao admitted. “The first time you have no baggage,” he said. “But now I have some sort of expectations and track record. I also want to prove to myself that I’m not a one hit wonder.”