Learning Visual Feature-Based World Models via Residual Latent Action

Name: Learning Visual Feature-Based World Models via Residual Latent Action
Uploaded: 2026-10-07T23:03:08.521403+00:00
Duration: 277 s
Description: This work introduces Residual Latent Action (RLA), a compact latent vector derived from DINO token residuals, to build an efficient visual world model. The RLA World Model predicts future visual features by using flow matching to predict this latent action, allowing for state-of-the-art prediction and enabling novel robot learning techniques.

This work introduces Residual Latent Action (RLA), a compact latent vector derived from DINO token residuals, to build an efficient visual world model. The RLA World Model predicts future visual features by using flow matching to predict this latent action, allowing for state-of-the-art prediction and enabling novel robot learning techniques.

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