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 brittle workflow glue across visual perception tools and simulators: manual tuning of visual foundation models, mesh cleanup, coordinate frame alignments, etc. We introduce \textit{Agentic Real2Sim}, a framework for generalized physical world modeling with vision-language agents that converts a real-world recording ...
| Authors | Guanxiong Chen, Qianjun Xia, Jiawei Peng et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 July 2026 |
| Last updated at source | 11 October 2026 |
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