Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents

Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a streamlined real2sim process must recover scene geometries and object states, infer physical parameters, and assemble actors, objects, cameras, poses, and trajectories into a runnable physical simulation. Today this process still depends on manual tuning of visual foundation models, mesh cleanup, coordinate-frame alignment, and brittle workflow glue across vi

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ethics.ai (21 July 2026), “Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents,” evidence record 12335, https://ethics.ai/record/12335 (originally published by arXiv).

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