# MaP-WAM：以记忆为规划的世界-动作建模框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-10 08:00
- AIHOT 分数：40
- AIHOT 链接：https://aihot.news/items/cmtwyrdba0aharouaxif88dx9
- 原文链接：https://arxiv.org/abs/2609.11561

## AI 摘要

研究者提出 MaP-WAM，一个将记忆依赖的世界-动作建模拆分为记忆锚定规划与规划条件执行的框架，把长期多模态情景上下文用作规划期证据，而非反复将完整历史输入执行器。该方法在 RMBench 上取得 83.3% 成功率，真实机器人任务成功率达 78.0%，且执行器推理延迟随任务历史增长基本保持恒定。

## 正文

Abstract:Mainstream robotic policies often adopt a Markovian formulation, but many complex real-world manipulation tasks are inherently non-Markovian, requiring long-horizon memory beyond the current observation. Existing memory mechanisms often rely on language summaries, growing visual windows, or their combinations, and may therefore lose fine-grained visual evidence or face a trade-off between history coverage and execution efficiency. We introduce MaP-WAM, a Memory-as-Plans framework that decomposes memory-dependent world-action modeling into memory-grounded planning and plan-conditioned execution, and uses long-term multimodal episodic context as planning-time evidence rather than repeatedly conditioning the executor on the full history. MaP-WAM represents memory as completed segment records containing language instructions and sparse visual context, and converts this episodic memory into compact plans comprising the next segment-level language plan and corresponding visual guidance. A World-Action-Progress (WAP) model executes each plan over an unknown duration by jointly predicting action chunks and corresponding execution progress at inference time, calibrating predicted progress through plan-observation alignment for adaptive segment transitions and closed-loop context updates. MaP-WAM keeps the executor context length fixed, while structured attention further enables key-value caching in both planning and execution. MaP-WAM achieves state-of-the-art performance on RMBench with an 83.3% success rate and attains 78.0% success on real-robot tasks, while maintaining approximately constant executor inference latency as task history grows.

Comments:

Subjects: Robotics (cs.RO)

Cite as: arXiv:2609.11561 [cs.RO]

(or arXiv:2609.11561v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.11561

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sizhe Zhao [

Thu, 10 Sep 2026 13:52:51 UTC (802 KB)

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