AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act. Action-labeled robot videos directly supervise control but are costly and limited in diversity, whereas egocentric human videos capture diverse interactions but lack robot actions and differ in embodiment and appearance. We introduce AffordanceWAM, an affordance-aware generative World Action Model that represents object-centric spatiotemporal affordance through Scalar Affordance and Affordance Heatmap, within the generated future World. This representation grounds visual prediction in task-relevant objects and interaction regions for action...
| Authors | Jiadi You, Qize Yu, Yue Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 September 2026 |
| Last updated at source | 22 September 2026 |
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