arXiv Machine Learning
D-JEPA: A Decision-Aligned Latent World Model
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Predicting Consequences and Reinforcing Navigation Policies with Latent World Models
World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primarily focus on reconstructing future observations or...
arXiv AI
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Toward Physically Grounded JEPA World Models for Goal-Conditioned Robotic Planning
The paper presents an end‑to‑end JEPA world model that enhances latent prediction with inverse dynamics and state alignment to improve goal‑conditioned robotic planning. By preventing latent collapse and grounding representations in physical configuration, the model achieves top success rates on tasks such as TwoRoom, PushT, and OGBench‑Cube, outperforming the baseline LeWorldModel. Ablation studies confirm that state alignment consistently boosts planning success over inverse dynamics alone across all four benchmark tasks.