AIM: Intent-Aware Unified world action Modeling with Spatial Value Maps
Pretrained video generation models provide strong priors for robot control, but existing unified world action models still struggle to decode reliable actions without substantial robot-specific training. We attribute this limitation to a structural mismatch: while video models capture how scenes evolve, action generation requires explicit reasoning about where to interact and the underlying manipulation intent. We introduce AIM, an intent-aware unified world action model that bridges this gap via an explicit spatial interface. Instead of decoding actions directly from future visual representations, AIM predicts an aligned spatial value map that encodes task-relevant interaction structure,...
| Authors | Liaoyuan Fan, Zetian Xu, Chen Cao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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