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://x.com/VerneRobotics/status/2118029255804129448/video/1

https://x.com/VerneRobotics/status/2118029256538169657/video/1