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.

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)
arXiv AI
5d ago

Beyond Action Prediction: Learning Search-Free Planning in Latent Space

The paper introduces Hindsight Inverse JEPA Planning (HIJP), a search‑free framework that replaces costly online action sequence optimization with a goal‑conditioned inverse model. HIJP trains by relabeling future observations as goals, providing action supervision across multiple horizons and shaping visual representations. The method also aligns predicted actions with a latent predictor model to enforce action‑transition consistency, achieving high success rates and significant speedups on visual control tasks.

By Guanchen Du, Jinhao Du, Yanpeng Sun, Shan Zhang, Na Zhao, Zechao Li