IM-ENGINE: Image Editing for Embodied Data Generation
Learning-based manipulation requires supervision that is both semantically meaningful and physically executable, but current data pipelines often provide only one of these properties. Human demonstrations capture intent but are costly to collect and constrained by the human-robot embodiment gap, while simulation can scale data generation but often under-specifies functional behavior. We present IM-ENGINE, a simulator-grounded pipeline that uses image editing as an intermediate representation for embodied data generation. Given a rendered scene with known geometry, depth, segmentation, and camera parameters, IM-ENGINE edits the image to inject task-relevant semantics, recovers explicit 3D ...
| Authors | Yian Wang, Junyi Cao, Xiaowen Qiu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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