OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation
Recent vision-language-action (VLA) models and world action models (WAMs) advance robotic manipulation by enriching intermediate representations with auxiliary spatial features or future visual-state prediction. However, these representations largely remain within the observation space and do not share the rigid-body geometry of the action space, forcing the action decoder to implicitly recover this geometry. We propose OASIS, a visuomotor policy that aligns the intermediate representation with the action space via $SE(3)$ end-effector trajectory prediction. OASIS couples a 3D-aware feature encoder that fuses vision-language and metric-depth features with an $SE(3)$ trajectory predictor t...
| Authors | Xinzhe Chen, Sihua Ren, Liqi Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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