Summary
A paper posted on zhichai.net introduces an arXiv preprint (2604.03208) by Wancong Zhang, Basile Terver, Artem Zholus and colleagues, scheduled for April 2026. The work targets a key weakness of model predictive control (MPC) with learned world models: long-horizon control, where accumulated prediction errors and an exponentially growing search space degrade performance. The authors propose learning latent world models at multiple temporal scales and performing hierarchical planning across these scales. This enables long-horizon reasoning while substantially reducing inference-time planning complexity, and the approach works as a modular planning abstraction compatible with diverse latent world-model architectures and domains. Empirically, the hierarchical method achieves zero-shot control on real-world non-greedy robotic tasks: using only final goal specifications, it reaches a 70% success rate on pick-and-place tasks, compared with 0% for a single-scale (flat) world model baseline. The forum post includes a Chinese-language abstract alongside the original English abstract and was auto-collected on 2026-04-06.
Paper Overview
- Field: Machine Learning
- Authors: Wancong Zhang, Basile Terver, Artem Zholus, et al.
- Release date: 2026-04-03
- arXiv: 2604.03208
Summary
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.
Approach
The paper addresses these challenges by:
- Learning latent world models at multiple temporal scales
- Performing hierarchical planning across these scales, enabling long-horizon reasoning while substantially reducing inference-time planning complexity
- Providing a modular planning abstraction that applies across diverse latent world-model architectures and domains
Results
The hierarchical approach enables zero-shot control on real-world non-greedy robotic tasks:
- 70% success rate on pick-and-place tasks using only final goal specifications
- Compared to 0% for single-scale (flat) latent world models
Links
- arXiv: https://arxiv.org/abs/2604.03208
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*Auto-collected on 2026-04-06.*
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