WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory
- Published
- Source
- arXiv
- Paper number
- 685
- Field
- Robotics
- arXiv ID
- 2607.18840
Key points
- It proposes a reasoning-augmented memory structure that combines short-term visual memory and long-term event memory.
- It builds a dataset of 5 million event segments called ManipEvent-5M.
- It can control robots through multiple modalities, including text, goal images, and demonstration videos.
- On a real dual-arm robot, it achieves 75 percent success on long-horizon tasks and 86.7 percent on fine-grained commands.
- Its generalization to new object categories improves sharply compared with prior work, from 43.3 percent to 60 percent OOD.
Paper links
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