DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
World-action models have shown promising robot-manipulation performance by jointly predicting future visual states and actions. However, existing methods mainly rely on short-term history and short-horizon future prediction, which is insufficient for long-horizon tasks whose correct execution depends on earlier observations and task progress. Such temporally dependent tasks require effective use of complementary temporal information, including recent local context, cross-stage historical events, immediate future dynamics, and global task progress. To address long-term forgetting and poor awareness of the global task state, we introduce DiM-WAM, a memory-augmented world-action model that i...
| Authors | Kai Wang, Zhaopeng Gu, Yixiang Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 June 2026 |
| Last updated at source | 12 July 2026 |
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