LeWAM: End-to-End World and Action Modeling with JEPAs
Overview
LeWAM improves success rates on contact-rich and long-horizon tasks, with faster planning. It is competitive in behavior cloning tasks. arXiv:2610.12818v1","latest":"LeWAM achieves 89.7% success on contact-rich tasks and 46.2% on long-horizon tasks, with 32.3x faster planning."}</think>
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- arXiv Robotics — research abstractsLeWAM: End-to-End World and Action Modeling with JEPAs
LeWAM is an end-to-end JEPA framework for control that improves success rates and planning efficiency in both simulated and physical environments. It achieves success rates of 89.7% on contact-rich tasks and 46.2% on long-horizon tasks, with 32.3 times faster planning. The model supports direct policy execution and efficient planning through learned latent dynamics, and is competitive with established methods in behavior cloning tasks.
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