J-LAW: Joint Localization and Action-Conditioned World Modeling via Coupled Latent Factor Graphs
Classical simultaneous localization and mapping (SLAM) estimates metric poses and a geometric map but does not provide an action-conditioned predictive state. Action-conditioned world models learn compact latent dynamics but ignore global metric consistency and accumulate drift under open-loop rollout. We introduce J-LAW (Joint Localization and Action-Conditioned World Modeling), a unified factor-graph formulation that connects metric pose variables, predictive latent states, and persistent latent landmarks in this letter.J-LAW represents each image as a compact predictive state and combines it with pose or motion measurements through a separately learned mapping. Its maximum a posteriori...
| Authors | Guanqun Cao, Liang Chen |
|---|---|
| Primary category | cs.RO |
| Published | 27 June 2026 |
| Last updated at source | 30 August 2026 |
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