Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control
Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting in representation space rather than reconstructing pixels, making them a natural backbone for latent model predictive control from offline demonstration logs. JEPA-style training optimizes short-horizon latent prediction, whereas planning requires a multi-step ranking of imagined futures by goal progress. Prior JEPA planners often inherit that ranking from embedding geometry, typically latent Euclidean distance, which arises as a byproduct of representation learning rather than as a progress cost mined from the logs. We propose Temporal-Distance-JEPA, which retains the LeWM encoder--predictor backbone and mi...
| Authors | Jiaxin Bai, Jiaxuan Xiong |
|---|---|
| Primary category | cs.CL |
| Published | 28 July 2026 |
| Last updated at source | 29 July 2026 |
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