Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
World model-based policy evaluation is a practical proxy for testing real-world robot control by rolling out candidate actions in action-conditioned video diffusion models. As these models increasingly adopt latent diffusion modeling (LDM), choosing the right latent space becomes critical. While the status quo uses autoencoding latent spaces like VAEs that are primarily trained for pixel reconstruction, recent work suggests benefits from pretrained encoders with representation-aligned semantic latent spaces. We systematically evaluate these latent spaces for action-conditioned LDM by comparing six reconstruction and semantic encoders to train world model variants under a fixed protocol on...
| Authors | Nilaksh, Saurav Jha, Artem Zholus et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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