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 that produces a camera-frame end-effector trajectory. Conditioned on the predictor's pose-supervised hidden states, the action decoder generates action chunks consistent with rigid-body motion. Across simulation and real-world experiments, OASIS outperforms VLA and WAM baselines in success rate and out-of-distribution generalization. Our project page is available at https://npuhandsome.github.io/OASIS_web.
Cite
@article{arxiv.2605.25829,
title = {OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation},
author = {Xinzhe Chen and Sihua Ren and Liqi Huang and Haowen Sun and Mingyang Li and Xingyu Chen and Zeyang Liu and Xuguang Lan},
journal= {arXiv preprint arXiv:2605.25829},
year = {2026}
}