arXiv Machine Learning
J-LAW: Joint Localization and Action-Conditioned World Modeling via Coupled Latent Factor Graphs
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arXiv Machine Learning
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J-LAW: Joint Localization and Actionable World Modeling via Coupled Latent Factor Graphs
arXiv:2606. 28712v1 Announce Type: cross Abstract: Classical SLAM estimates metric poses and a geometric map but produces no actionable predictive model for planning.
arXiv AI
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Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
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A Generalization Theory for JEPA-Based World Models
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Predicting Consequences and Reinforcing Navigation Policies with Latent World Models
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WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.