VideoVLA: Video Generators Can Be Generalizable Robot Manipulators

Understand

Generalization in robot manipulation is essential for deploying robots in open-world environments and advancing toward artificial general intelligence.

• While recent Vision-Language-Action (VLA) models leverage large pre-trained understanding models for perception and instruction following, their ability to generalize to novel tasks, objects, and settings remains limited.
• In this work, we present VideoVLA, a simple approach that explores the potential of transforming large video generation models into robotic VLA manipulators.
• Given a language instruction and an image, VideoVLA predicts an action sequence as well as the future visual outcomes.