arXiv AI
LeWAM: End-to-End World and Action Modeling with JEPAs
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arXiv AI
4d ago
LeWAM: A JEPA World Action Model with Diffusion-Steering-Based MPC
LeWAM is a bidirectional transformer that predicts forward, backward, inverse dynamics and policy, trained end‑to‑end on a decoder‑free JEPA latent. It outperforms a forward‑only JEPA world model by better aligning latent representations with robot and object states while ignoring visual distractors. In closed‑loop tests, LeWAM matches a flow‑matching policy of the same size and improves MPC planning by sampling actions in the policy head’s noise space rather than raw actions.
arXiv AI
Jun 16
LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
arXiv:2606. 15768v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene.
arXiv AI
Jul 1
Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding
arXiv:2606. 31232v1 Announce Type: new Abstract: Learning visual world models for planning requires compact latent dynamics that remain sensitive to actions, yet reconstruction-free joint-embedding objectives can collapse to action-insensitive representations.
arXiv AI
Oct 5
Latent Goal Prediction from Language for Model-Based Planning
arXiv:2606.20627v2 Announce Type: replace Abstract: Joint-Embedding Predictive Architectures (JEPAs) enable agents to plan in latent space by imagining the outcomes of candidate actions, yet task spe...
arXiv AI
Sep 4
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.
Hugging Face Trending Papers
Sep 3
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.