SOURCE-LINKED INTELLIGENCE
DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation
Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and rotational actions are unreliable for full 7-DoF end-effector control. To address this gap, we introduce DUET-DINO, a simultaneous cross-view latent world model that jointly learns action-conditioned predictions from static side- and wrist-camera observations through cross-view conditioning. By exploiting complementary global scene and gripper-centric information, DUET-DINO enables latent planning o
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-09T17:41:38.000Z
First collected: 2026-09-20T19:32:24.350Z. This is not the publication date.