JEPA-WMs for Physical Planning
Published:Dec 30, 2025 22:50
•1 min read
•ArXiv
Analysis
This paper investigates the effectiveness of Joint-Embedding Predictive World Models (JEPA-WMs) for physical planning in AI. It focuses on understanding the key components that contribute to the success of these models, including architecture, training objectives, and planning algorithms. The research is significant because it aims to improve the ability of AI agents to solve physical tasks and generalize to new environments, a long-standing challenge in the field. The study's comprehensive approach, using both simulated and real-world data, and the proposal of an improved model, contribute to advancing the state-of-the-art in this area.
Key Takeaways
- •JEPA-WMs are a promising approach for physical planning in AI.
- •The paper investigates the impact of model architecture, training objective, and planning algorithm.
- •The proposed model outperforms existing baselines in both navigation and manipulation tasks.
- •Code, data, and checkpoints are publicly available.
Reference
“The paper proposes a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks.”