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Predicting future physical states stands as a central challenge in embodied intelligence. Existing video world models leave 3D dynamics implicit, action-based formulations remain tied to specific robot configurations, and 3D motion prediction models use flow-only supervision, providing a limited learning signal for phy...
Scaling robotic foundation models requires diverse training data and reliable evaluation environments. Simulation offers a scalable solution, yet existing generation pipelines remain constrained by predefined assets and skills, a disconnect between scene generation and task generation, and limited support for complex e...
Function-Room Generation is introduced, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than merely visually plausible layouts, and ScenePRM, an execution-grounded process reward framework that improves the expert through reinforcement learning with functional, ge...
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