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Robotics

, Better, Faster, Longer: An Effective World Model for Robotic Manipulation

Arnav Kumar Jain, Suneel Belkhale, Sergey Levine

Featured June 14, 2026

Original Paper

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Simply

By combining smart ways to predict future scenes and robot movements, WEAVER helps robots imagine what will happen next, making them better at learning new tasks and planning actions much faster than before.

In depth
The paper introduces WEAVER, a novel world model architecture designed for robotic manipulation that simultaneously achieves high fidelity, long-horizon consistency, and efficient generation. It accomplishes this by fusing key design decisions from video generation (flow matching, diffusion forcing, pretrained encoders) and latent world models (future latent prediction, reward/critic heads), alongside a multi-view memory architecture to handle complex robotic interactions.

Key Takeaways

  • 1
    WEAVER achieves a trifecta of high fidelity, long-horizon consistency, and efficient generation, which prior robotic world models struggled to balance.
  • 2
    The model leverages flow matching and diffusion forcing from video generation, combined with a multi-view memory architecture, to enable fast and coherent predictions over extended periods.
  • 3
    WEAVER significantly improves policy evaluation, policy improvement (38% success rate increase), and test-time planning (5-10x speedup) on real robotic hardware, demonstrating its practical utility.

Conceptual Flow

HIGH LEVEL
1
Methodology: How WEAVER Predicts the Future

WEAVER watches what the robot sees and does, remembers past events, and then quickly guesses what will happen next in a simplified way.

Robot Sees
Robot Does
Past Memories
Imagine Future
What Happens Next
How Good It Is
2
Results: Better Robot Learning and Planning

Because WEAVER can imagine the future so well and quickly, robots can learn new skills and plan their moves without needing lots of real-world practice.

Accurate Future Guess
Consistent Over Time
Very Fast Guessing
Robot Benefits
Evaluate Actions
Improve Skills
Plan in Real-Time