Model predictive control (MPC) with learned world models has emerged as a promising paradigm for embodied control, particularly for its ability to generalize zero-shot when deployed in new environments. However, learned world models often struggle with long-horizon control due to the accumulation of prediction errors and the exponentially growing search space. In this work, we address these challenges by learning latent world models at multiple temporal scales and performing hierarchical planning across these scales, enabling long-horizon reasoning while substantially reducing inference-time planning complexity. Our approach serves as a modular planning abstraction that applies across diverse latent world-model architectures and domains. We demonstrate that this hierarchical approach enables zero-shot control on real-world non-greedy robotic tasks, achieving a 70% success rate on pick-&-place using only a final goal specification, compared to 0% for a single-level world model. In addition, across physics-based simulated environments including push manipulation and maze navigation, hierarchical planning achieves higher success while requiring up to 4x less planning-time compute.

翻译：基于学习的世界模型进行模型预测控制（MPC）已成为具身控制领域的一种有前景范式，特别是在新环境中部署时展现出的零样本泛化能力尤为突出。然而，学习到的世界模型在长时域控制中常因预测误差累积和搜索空间指数级增长而面临挑战。本研究通过在多时间尺度上学习潜在世界模型并跨尺度执行分层规划来应对这些挑战，从而在显著降低推理时规划复杂度的同时实现长时域推理。本方法作为一种模块化规划抽象机制，可适用于多种潜在世界模型架构和应用领域。我们证明了这种分层方法能够在真实世界非贪婪机器人任务中实现零样本控制：在仅使用最终目标规范的情况下，拾放操作成功率达70%，而单层级世界模型的成功率为0%。此外，在包含推操作与迷宫导航的物理模拟环境中，分层规划实现了更高的成功率，同时所需规划计算量减少高达4倍。