← Back to all news
arXiv AI By Shashank Hegde, Alexander Popov, Elie Aljalbout, Nikolai Smolyanskiy

LeWAM: A JEPA World Action Model with Diffusion-Steering-Based MPC

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Jun 26

Fast LeWorldModel

arXiv:2606. 26217v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs), including recent LeWorldModel (LeWM), have become a promising foundation for reconstruction-free visual world models.

By Yuntian Gao, Xiangyu Xu
rag
More like this →
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.

By Jialei Chen, Kai Wang, Kang Chen, Shuaihang Chen, Feng Gao, Wenhao Tang, Zhiyuan Li, Weilin Liu, Zhuyu Yao, Boxun Li, Yuanbo Xu, Chao Yu
diffusionroboticsmultimodalbenchmarks
More like this →
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.

By Muyuan Liu (GENISOM AI, Beijing, China), Yue Huang (GENISOM AI, Beijing, China), Zheng Liang (GENISOM AI, Beijing, China), Xiang Gao (GENISOM AI, Beijing, China)
roboticsbenchmarkssafety
More like this →