---
title: "Hierarchical World Models with Implicit Dynamics"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.42146v1"
permalink: "https://feed.astralor.com/items/itm_64f418c762639485096478c8"
published: "2026-10-30T17:59:25.000Z"
score: 56
channel: "tech"
tags: ["AI Agent","RAG","多模态","Hierarchical World Models","Implicit Dynamics Planning","However","Planning","Existing"]
---

# Hierarchical World Models with Implicit Dynamics

## 速览

Planning with world models enables test-time adaptation for embodied agents, offering superior generalization over direct policy learning. However, long-horizon tasks remain hindered by cumulative errors and search comp…

## 关键点

- Planning with world models enables test-time adaptation for embodied agents, offering superior generalization over direct policy learning.
- However, long-horizon tasks remain hindered by cumulative errors and search complexity.
- Existing hierarchical methods often use rigid macro-actions or latent skills, leading to brittle representations and limited fine-grained control.

## 全文

Planning with world models enables test-time adaptation for embodied agents, offering superior generalization over direct policy learning. However, long-horizon tasks remain hindered by cumulative errors and search complexity. Existing hierarchical methods often use rigid macro-actions or latent skills, leading to brittle representations and limited fine-grained control. We introduce ID-WM (Implicit Dynamics World Models), a hierarchical world model that decouples state-space reachability from local control. Our framework utilizes a high-level model to sample feasible transitions across the state manifold and a low-level world model as an inverse dynamics solver to ground these transitions into precise actions. This allows the planner to identify reachable subgoals without being constrained by low-level execution details. By prioritizing local physical feasibility over global behavioral patterns, ID-WM synthesizes novel long-horizon plans from short segments, transcending the specific behavioral modes seen during training. We demonstrate its effectiveness across three navigation and manipulation suites, achieving a 38.5% improvement over state-of-the-art policy and diffusion-based planners. Crucially, by leveraging this hierarchical structure to mitigate compounding errors, our approach yields a 135% performance gain over single-level world model MPC, particularly in complex, unseen configurations for long-horizon tasks. Videos can be viewed at https://id-wm.github.io

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_64f418c762639485096478c8