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Rohan Paul· @rohanpaul_ai · X· · Original publication time AI score47

Verne Robotics releases open-weight robot model V0.5Machine translation

AI introduction

V0.5 uses camera input to predict the next second of arm, gripper and mobile-base motion. Verne Robotics says it learned new industrial tasks from under two hours of demonstrations. In its RoboTwin 2.0 tests, randomized clutter, lighting and textures reduced its score from 81.0% to 73.4%.

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@rohanpaul_ai

Brilliant release by @VerneRobotics

V0.5, an open-weight world action model, that looks through cameras and outputs the next second of arm, gripper and mobile-base motion.

The big deal is that it learned new industrial tasks from under 2 hours of demonstrations.

> Robot models usually stumble when a room changes. With clutter and new lighting that neither model trained on, Verne Robotics’ V0.5 lost 7.6 points in simulation, while Physical Intelligence’s π0.5 lost 24.7.

> On RoboTwin 2.0, randomized clutter, lighting and textures cut V0.5 from 81.0% to 73.4% in Verne’s tests, while Physical Intelligence’s π0.5 fell from 70.7% to 46.0%.

> Built on the Wan2.2 video generator, V0.5 learned from over 1.7M demonstrations to predict upcoming camera frames alongside the motion producing them.

https://t.co/BaTwYMcNsj

https://t.co/8WKzZHXJIw

QuoteVerne@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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Source: Rohan Paul · x.com

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