arXiv AI By Gaoyue Zhou, Yvonne Wu, Zichen Jeff Cui, Nicolas Ballas, Mido Assran, Lerrel Pinto, Yann LeCun

Hierarchical World Models with Implicit Dynamics

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Hierarchical World Models with Implicit Dynamics (ID‑WM) introduces a two‑level approach that separates state‑space reachability from local control. A high‑level model samples feasible transitions across the state manifold, while a low‑level world model acts as an inverse dynamics solver to translate these transitions into precise actions. This design enables planners to identify reachable subgoals without being constrained by low‑level execution details, producing novel long‑horizon plans that outperform state‑of‑the‑art policy and diffusion‑based planners by 38.5% and single‑level MPC by 135% in complex, unseen configurations.

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arXiv AI
Sep 3

What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?

The paper investigates Joint-Embedding Predictive World Models (JEPA-WMs), a class of methods that perform planning in a learned representation space rather than raw input space. It systematically studies how model architecture, training objectives, and planning algorithms influence success across simulated and real‑world robotic tasks, and proposes a JEPA-WM variant that surpasses established baselines in navigation and manipulation. The authors provide code, data, and checkpoints for reproducibility.

By Basile Terver, Tsung-Yen Yang, Jean Ponce, Adrien Bardes, Yann LeCun