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@VerneRobotics

Verne builds general‑purpose physical AI. We deploy robots to expand the productive capacity of the physical world.
vernerobotics.com Joined June 2025
9 Following 2.4K Followers 30 Posts

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Verne Verified account @VerneRobotics
Introducing V0.5, Verne’s open-weight world action model for industrial robots.

We believe democratizing robotics starts with making frontier foundation models open to everyone.

Bringing robots to every industry will take many teams, building on shared progress across many workflows.

V0.5 is the leading US-made open-weight model on LIBERO and RoboTwin 2.0 benchmarks.

We hope you’ll join us in putting V0.5 to work.
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68 replies 102 reposts 479 likes 68K views View on X
Verne Verified account @VerneRobotics
The next generation of robots will learn from the physical world.

From factory floors to farmers markets, Verne is turning everyday human work into valuable robotics training data.

Whether you're looking to automate your operations or monetize the physical data your business generates, we'd love to connect.
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4 replies 9 reposts 40 likes 47K views View on X
Verne Verified account @VerneRobotics
Robotics is not going to be solved by one type of data. @neil_nie_ sits down to share @VerneRobotics's thinking around robot learning across varied data sources.

Different stages of robot learning call for different data modalities. Egocentric human video can provide broad priors. Hand-held data collection devices can efficiently capture dexterous manipulation. Teleoperation remains one of the best ways to collect high-quality robot demonstrations for many tasks. As models improve, other sources of data will become increasingly important as well.

The challenge isn’t choosing one approach; it’s building a policy learning pipeline that supports all of them.

At @VerneRobotics, we've designed our stack around that philosophy: from data collection, to model training, to deployment. Supporting multiple data modalities gives us the flexibility to use the right tool for each stage of learning, iterate more quickly as requirements evolve, and continuously improve robots after deployment.

The winning robotics companies won't be defined by a single data collection method. They'll be defined by how well they integrate many of them into one learning system.
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