Splat2Act: From Captured Scenes to Personalized VLA Manipulation
A user who brings a generalist manipulation policy into their home or office wants it to pick up their objects from their shelves, counters, and tables. Yet even a strong generalist like $π_{0.5}$ achieves 0% pickup success in our captured deployment scenes without adaptation, and collecting real demonstrations in every user's environment is prohibitively expensive. We present Splat2Act, an automated real-to-sim-to-real pipeline that personalizes a pretrained vision-language-action (VLA) policy to a specific scene and object set using only a handheld phone scan and images of the user's objects. Unlike prior real-to-sim work that reconstructs individual tabletops, Splat2Act operates on bui...
| Authors | Xiaomeng Ye, Krishna Jaganathan, Gyubeum Lim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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