English(EN) World-Action Models for Robot Learning and Control: A Survey

综述梳理机器人智能的世界-动作模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

这篇综述论文全面回顾了用于机器人学习与控制的世界-动作模型（WAMs）。它将现有方法组织成一个统一的分类体系，涵盖了表示、转移建模、动作接口、架构、训练流程、数据模态和扩展策略。论文还探讨了WAM在操作、导航和自动驾驶中的应用，并讨论了诸如动作对齐、空间一致性和长时记忆等关键挑战。目标是为整合预测性世界建模与动作生成奠定技术基础，以增强具身机器人的智能。 AI

影响为在机器人中整合预测性世界建模与动作生成提供了基础技术概述。

排序理由该条目是一篇发表在arXiv上的综述论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Zuxing Lu, Hongjia Zhai, Guanzhi Wang, Huajian Zeng, Jiaqi Yang, Jingyu Liu, Lei Cheng, Yuantai Zhang, Yuheng Qiu, Zezhou Cheng, Ivan Laptev, Danfei Xu, Benjamin Riviere, Giuseppe Loianno, Eric Xing, Xingxing Zuo · 2026-09-16 04:00

用于机器人学习与控制的世界动作模型：一项调查

arXiv:2609.16074v1 Announce Type: cross Abstract: Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations and language instructions to actions, they must anticipate how candidate actions m…

本文如何被排名

报道来源 [1]

用于机器人学习与控制的世界动作模型：一项调查

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