Summary
This paper addresses the challenges of long-horizon control in model predictive control (MPC) with learned world models, which often struggle with accumulating prediction errors and large search spaces. It proposes a solution by learning latent world models at multiple temporal scales and performing hierarchical planning. This approach aims to improve the generalization and long-term planning capabilities of embodied AI systems.
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