arXiv AI
Does Latent Planning Survive Point Clouds? Action-Conditioned JEPA World Models for Geometric Observations
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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.
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Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
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Hugging Face Trending Papers
5d ago
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 better support goal‑conditioned robotic planning. By incorporating inverse dynamics, the model prevents latent collapse and encodes action information, while state alignment ties consecutive latent states to their physical configurations and motions. Experiments on four benchmark tasks show the model achieves top success rates on TwoRoom, PushT, and OGBench‑Cube, and its state alignment consistently improves planning performance over inverse dynamics alone.