Mbodi’s Vision for Smarter Robot Training

Mbodi was founded by Xavier Chi and Sebastian Peralta, engineers who previously worked at Google. While at Google they recognized that advances in artificial intelligence were moving toward physical applications, yet there was no efficient way to train robots for new, dynamic tasks. Their answer is a hybrid cloud‑to‑edge platform that integrates with existing robotic stacks and relies on a network of AI agents that communicate to gather the information needed for rapid robot training.

How the Platform Works

The system accepts natural‑language prompts from users. Once a request is received, Mbodi’s software decomposes the request into smaller subtasks. Each AI agent tackles a portion of the problem, collecting data, generating instructions, or refining motions. By dividing and conquering, the agents accelerate the learning loop, allowing robots to adapt to tasks such as picking, packaging, or even more complex operations without extensive reprogramming.

Key Milestones and Partnerships

Since its launch in 2024, Mbodi has focused on the picking and packaging segment. The startup earned recognition by winning the ABB Robotics AI startup competition, which secured a partnership with a Swiss robotics organization that was later acquired by SoftBank. Building on that momentum, Mbodi is currently conducting a proof‑of‑concept deployment with a Fortune 100 consumer and product‑goods company, addressing the challenge of constantly changing packaging configurations that make traditional robot reprogramming impractical.

TechCrunch Disrupt Showcase

Mbodi will present its technology as a finalist in the Top 20 Startup Battlefield at TechCrunch Disrupt 2025, held in San Francisco from October 27‑29. The event will give the company a stage to demonstrate how its AI‑agent framework can reduce the time and effort required to train robots for new tasks, positioning Mbodi as a practical alternative to research‑lab‑only solutions.

Future Outlook

The founders emphasize that Mbodi is not a research lab; they aim to deliver a production‑ready system that works reliably at scale. While the company plans broader deployments in 2026, its immediate focus is on refining the platform through real‑world use cases and expanding partnerships across industries that demand flexible, adaptable robotic solutions.

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