arXiv 2606.080292026-06-06cs.RO

IntentNav: 从人类演示中学习空间-视觉物体导航

IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations

Nanyang Technological University（南洋理工大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；A*STAR Institute for Infocomm Research (I2R)（新加坡科技研究局资讯通信研究院）

Yuxin Cai, Zongtai Li, Maonan Wang, Muyi Bao, Haokun Zhu, Ruofei Bai, Ding Zhao, Zirui Li, Wenshan Wang, Wei-Yun Yau, Ji Zhang, Chen Lv

备注：: 26 pages, 9 figures

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

提出IntentNav框架，通过人类演示学习类人物体导航策略，利用前沿标注和意图对齐目标实现最优性能，并零样本迁移到多种机器人平台。

摘要：

物体导航要求机器人在未知环境中搜索未观察到的目标，通过在部分可观测性下决定下一步探索位置。有效的搜索类似于人类探索：选择性探查视觉上有希望的前沿，同时依赖空间记忆避免重复访问。我们提出IntentNav，一个从人类演示中学习类人ObjectNav策略的空间-视觉模仿框架。为了从低级人类动作推断高级搜索意图，我们引入了基于前沿的人类意图标注，该方法前瞻人类演示并标注最能解释演示者未来搜索方向的前沿。我们构建了一个空间-视觉候选空间，其中BEV记忆跟踪已探索区域、未探索前沿和轨迹历史，而自我中心视觉记忆为每个候选提供语义线索。训练一个VLM策略在这些基于上下文的候选中进行选择，使用意图对齐目标以鼓励一致且类人的探索。IntentNav在MP3D、HM3D-v1和HM3D-v2 ObjectNav基准上实现了最先进的性能。所提出的候选级导航界面无需进一步VLM微调即可零样本迁移到轮式、四足和类人机器人。\href{https://anonymous.4open.science/w/IntentNav/}{项目页面}。

英文摘要：

Object navigation requires a robot to search for an unobserved target in an unknown environment by deciding where to explore next under partial observability. Effective search resembles human-like exploration: selectively probing visually promising frontiers while relying on spatial memory to avoid redundant revisits. We propose IntentNav, a spatial-visual imitation framework that learns human-like ObjectNav policies from human demonstrations. To infer high-level search intent from low-level human actions, we introduce Frontier-based Human-Intent Labeling, which looks ahead in human demonstrations and labels the frontier that best explains the demonstrator's future search direction. We construct a spatial-visual candidate space, where BEV memory tracks explored regions, unexplored frontiers, and trajectory history, while egocentric visual memory provides semantic cues for each candidate. A VLM policy is trained to select among these grounded candidates, using Intent-Aligned Objective to encourage consistent and human-like exploration. IntentNav achieves state-of-the-art performance on the MP3D, HM3D-v1 and HM3D-v2 ObjectNav benchmarks. The proposed candidate-level navigation interface transfers zero-shot to wheeled, quadruped, and humanoid robots without further VLM fine-tuning. \href{https://anonymous.4open.science/w/IntentNav/}{Project page}.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。