Not All Actions Are Equal: Rethinking Conditioning for Dexterous World Model
Recent advances in action-conditioned world models show promising progress in modeling complex interactions and forecasting future states under diverse action sequences. While these models are often driven by stronger visual representations and model capacity, action conditioning itself remains underexplored. Most existing approaches compress the entire action sequence into a single representation, which works well for low-DoF control but becomes less reliable in high-DoF scenarios. We observe that high-DoF dexterous actions are inherently heterogeneous, spanning multiple orders of magnitude, where large-scale motions coexist with subtle but important signals. When uniformly aggregated, o...
| Authors | Zizhao Yuan, Zhengtu Liang, Taowen Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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