SCOUT: Planning under Occlusion via Object-Centric World Model Rollouts
Overview
This research presents SCOUT, an occlusion-aware planning framework that bridges high-level deliberation with grounded physical rollouts. By leveraging an object-centric scene representation to reconstruct hidden geometry and using a learned action-conditioned world model evaluated via Monte Carlo Tree Search, SCOUT outperforms Vision-Language-Action policies on 'Reveal' and 'Placement' tasks in cluttered environments. The framework was validated through real-world robotic manipulation in a cluttered shelf setting.
Generated from attributed reports · Updated 3 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Dataset · tasks: Reveal · Differing source assertions
“-conditioned world model. Evaluations on 'Reveal' and 'Placement' tasks show that SCOUT outperforms Vision-Language-Action (VLA) policies, embodied agents, and 3D world models, with significant gains in long-horizon tasks requiring reasoning over occluded geometry. We further validate the framework's robustness through real-world robotic manipulation in a cluttered shelf environment.”
Exact source · revision 1Source owner not reported
Dataset · tasks: Placement · Differing source assertions
“-conditioned world model. Evaluations on 'Reveal' and 'Placement' tasks show that SCOUT outperforms Vision-Language-Action (VLA) policies, embodied agents, and 3D world models, with significant gains in long-horizon tasks requiring reasoning over occluded geometry. We further validate the framework's robustness through real-world robotic manipulation in a cluttered shelf environment.”
Exact source · revision 1Source owner not reported
Dataset · embodiments: robotic manipulation
“-conditioned world model. Evaluations on 'Reveal' and 'Placement' tasks show that SCOUT outperforms Vision-Language-Action (VLA) policies, embodied agents, and 3D world models, with significant gains in long-horizon tasks requiring reasoning over occluded geometry. We further validate the framework's robustness through real-world robotic manipulation in a cluttered shelf environment.”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalSCOUT: Planning under Occlusion via Object-Centric World Model Rollouts
This research presents SCOUT, an occlusion-aware planning framework that bridges high-level deliberation with grounded physical rollouts. By leveraging an object-centric scene representation to reconstruct hidden geometry and using a learned action-conditioned world model evaluated via Monte Carlo Tree Search, SCOUT outperforms Vision-Language-Action policies on 'Reveal' and 'Placement' tasks in cluttered environments. The framework was validated through real-world robotic manipulation in a cluttered shelf setting.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).