Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control
Temporal-Distance-JEPA trains a planner’s latent space with temporal progress mined from offline trajectories.
The paper says standard JEPA planners often rely on embedding distance as an accidental proxy for goal progress. Its method adds directed temporal cost supervision from same-trajectory ordering, cross-trajectory negatives, and rollout consistency. In locked evaluation, the mined cost reaches 100.0% success on Two-Room versus LeWM’s 97.4%, while the temporally trained checkpoint also improves Euclidean planning on OGB-Cube and Push-T. HF Daily Papers' note
The paper says standard JEPA planners often rely on embedding distance as an accidental proxy for goal progress. Its method adds directed temporal cost supervision from same-trajectory ordering, cross-trajectory negatives, and rollout consistency. In locked evaluation, the mined cost reaches 100.0% success on Two-Room versus LeWM’s 97.4%, while the temporally trained checkpoint also improves Euclidean planning on OGB-Cube and Push-T. HF Daily Papers' note
score 5