English(EN) MessyNav: Zero-Shot Navigation via Manipulation in Messy Environments

机器人通过 MessyNav 在混乱环境中学会零样本导航

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员推出 MessyNav，一个专为机器人在复杂、混乱环境中导航而设计的新型零样本框架。该系统利用视觉语言模型 (VLM) 进行显式的可供性推理，使机器人能够决定是操纵、避开还是与障碍物进行交互。在真实世界试验中，MessyNav 成功率显著提高，比现有方法高出 27 个百分点，且无需进行任务特定训练。 AI

影响通过智能地与障碍物交互，使机器人能够导航复杂的真实世界环境，可能加速自主系统的部署。

排序理由这是一篇详细介绍使用视觉语言模型进行机器人技术新框架的研究论文。[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) · Kenneth Llontop, Cherie Ho, Dylan Zhou, Anuva Banwasi, Priya Sundaresan, Carlota Par\'es-Morlans, Jeannette Bohg · 2026-11-06 05:00

MessyNav：在混乱环境中通过操纵实现零样本导航

arXiv:2611.07417v1 Announce Type: cross Abstract: Real-world environments are messy, with paths often blocked by everyday objects like chairs and toys, requiring robots to perform interactive navigation by manipulating obstacles to clear a path. Existing methods rely on extensive…

本文如何被排名

报道来源 [1]

MessyNav：在混乱环境中通过操纵实现零样本导航

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