SMWM trains end-to-end latent world models from offline reward-free data using inverse dynamics regularization to prevent collapse and align states with controllable actions for planning.
Enhancing policy learning with world-action model.arXiv preprint arXiv:2603.28955, 2026
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Temporal video pretraining induces stronger action-relevant structure in video world model latents than pixel reconstruction, as shown by inverse-dynamics probing across encoder families.
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Sensorimotor World Models: Perception for Action via Inverse Dynamics
SMWM trains end-to-end latent world models from offline reward-free data using inverse dynamics regularization to prevent collapse and align states with controllable actions for planning.