70

Out of 100

N/A

Post-money

$222M

All rounds

70/100

2017

100-500 employees

March 2026

Covariant builds an AI robotic picking and automation platform that enables warehouse and fulfillment robots to handle the enormous variety of product shapes, sizes, and packaging types encountered in real-world logistics operations. The platform is built on RFM-1, a foundation model for robotics trained on one of the largest robotics datasets ever assembled, enabling generalized manipulation capa

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P

Pieter Abbeel

Founder & CEO

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StageSeries C
Employees100-500
Country🇺🇸 United States

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Series C · No public funding round data available yet.

Frequently Asked Questions

What is Covariant's valuation?
Covariant's valuation is not publicly disclosed.
Who invested in Covariant?
Investor information for Covariant is not publicly available at this time.
When did Covariant last raise funding?
No public funding round data is currently available for Covariant.
How many employees does Covariant have?
Covariant has approximately 100-500 employees.
What does Covariant do?
Covariant builds an AI robotic picking and automation platform that enables warehouse and fulfillment robots to handle the enormous variety of product shapes, sizes, and packaging types encountered in real-world logistics operations. The platform is built on RFM-1, a foundation model for robotics trained on one of the largest robotics datasets ever assembled, enabling generalized manipulation capabilities across new product types without task-specific retraining.\n\nThe company raised approximately 222 million USD and has deployed its AI in warehouse environments at major retailers and logistics operators in North America and Europe, with robots handling millions of picks per day across diverse SKU catalogs. Covariant was founded by researchers from UC Berkeley with foundational backgrounds in deep reinforcement learning for robotic manipulation.\n\nIntelligent robotic picking remains one of the hardest unsolved problems in warehouse automation, as the combinatorial variety of product types encountered in e-commerce fulfillment exceeds what rule-based vision systems can handle reliably. Covariant approach of training a generalist manipulation model on large-scale real-world robotics data parallels the approach that made large language models broadly capable, and represents one of the most technically credible attempts to bring general robot AI to industrial deployment at scale.