Robots can be programmed to do a variety of tasks, like packing boxes and even performing surgery. But each individual movement or task requires its own specific training process, which makes it hard for robots to adapt in real-world scenarios.
Mbodi wants to make training robots easier and quicker with the help of AI agents. The company will be showcasing this tech as one of the Top 20 Startup Battlefield finalists at TechCrunch Disrupt 2025.
New York-based Mbodi built a cloud-to-edge system, a hybrid computing system using both cloud and local compute, that is designed to integrate into existing robotic tech stacks. The software relies on a multitude of AI agents that communicate with each other to gather the needed information to help a robot learn a task faster.
Once deployed, Mbodi will collect data and learn from its real-world use cases.
Xavier Chi, co-founder of Mbodi, told TechCrunch that users prompt the software using natural language, and Mbodi breaks down the request into smaller subtasks. Mbodi’s cluster of agents essentially divides and conquers the task to gather the needed information to train the robot on the prompt quickly.
“The tricky thing with the physical world, it’s infinite possibility,” Chi said. “Every time you can invent something completely new, you haven’t had any data, that is a problem in the physical world. We always need to have a system where you can orchestrate different models or have anyone correct a robot and tell it to do certain things certain ways.”
Chi said he and co-founder Sebastian Peralta got the idea for the company while working as engineers at Google. While they weren’t working on robotics, they both came to the realization that the advancements in AI were heading to the physical world and despite a rise in physical AI, there still wasn’t a great way to quickly train robots.
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