Been seeing a lot of Astra for real2sim, but what you need for robotics: physics and affordance. Our SOTA recipe specifically solves for: • Affordance: model object details that convey functionality. • Physics: calibrate simulation dynamics against real-world observations. Our agent has the action space of a “full-stack simulation engineer” and jointly considers geometry, materials, colliders, articulation, and physics. It automatically calibrates physics against real trajectories and matches task-specific dynamics. Simulation environments is useful only if it can provide meaningful policy level signals. Methods, comparisons, and results below.

Introducing a world built by the Moonlake's world model. 🏙️ Most world models only allow for a limited action space. Moonlake maintains multimodal states across physics, appearance, geometry, and casual effects and predict how they evolve under different actions. 👇