PLaCA: Learning Patch-Level Future Latent Change for Action Generation
Vision-Language-Action (VLA) models generate robot actions from visual observations and language instructions, but effective manipulation also requires anticipating how the environment will change. Existing world-action models explore future image prediction and latent dynamics modeling. However, generating complete future images can be computationally expensive, while highly compressed latent representations may obscure local changes. To address these challenges, we propose PLaCA, a world-to-action framework built around future latent change. PLaCA directly predicts spatially structured patch-level future latent change from the current multimodal input, preserving local information about...
| Authors | Zhan Yi, Yuchen Wang, Chao Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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