SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
- Published
- Source
- arXiv
- Paper number
- 525
- Field
- Robotics
- arXiv ID
- 2606.28276
Key points
- It automatically generates a sim-ready digital twin from a single video, with F1 scores from 0.81 to 0.92 and 0.93 to 0.99 after 3 minutes of tuning.
- It generates scene variations that preserve affordances while increasing diversity across object, scene, and task cousins.
- Across 7 tasks and 5 policies, simulation evaluation correlates with real performance at Pearson 0.911, which is 0.59 higher than the previous SOTA.
- Training policies on cousin data improves zero-shot sim-to-real transfer success by 17 to 40 percent.
- It shows strong zero-shot transfer on YAM, with 99 percent Pot on Stove success, and on DROID, with 100 percent Stack Dishware success.
- It handles more complex tasks than prior work, including multi-step manipulation, articulated-object interaction, and bimanual collaboration.
Paper links
External research summaries. These are not HDATF publications or measured product results.