What Drives Success in Physical Planning with JEPA World Models? (arxiv.org)

Researchers have made significant strides in AI physical planning by investigating Joint-Embedding Predictive World Models (JEPA-WMs). This work addresses a critical challenge in developing agents that can adapt to new physical tasks and environments by leveraging world models trained from state-action trajectories. The study highlights a novel approach in which planning algorithms operate in the learned representation space of world models, allowing for more efficient task execution by abstracting away irrelevant details. Through experiments in both simulated and real-world settings, the researchers evaluated various components, such as model architecture and training objectives, to identify the most effective strategies. Their findings culminated in a new model that outperformed established benchmarks, DINO-WM and V-JEPA-2-AC, across navigation and manipulation tasks. This advancement not only enhances the capability of AI systems in diverse environments but also opens avenues for more sophisticated planning techniques, signaling a promising direction for future AI/ML research and applications. Code and data related to this study are publicly available, encouraging further exploration and development within the community.