arXiv cs.AI· Jing Xia, Changyan Li, Yuduo Wang, Guiyin Tian, Bo Wang, Yifan Liao, Mingjun Li, Shiyan Hu·· 2 天前AI 评分29
AVALON：零样本室内导航，用人类播种的拓扑记忆实现
AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory
AI 导读
AVALON 是一个面向"人熟悉、机器人未见"室内场景的拓扑导航框架，机器人无需事先实地遍历或站点专项训练，仅需平面图、地点线索和初始拓扑-里程计对齐即可部署。
正文 · 原文
Abstract:Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users. We address this human-familiar, robot-unseen setting with AVALON, a topological navigation framework organized around human-seeded layered spatial memory, node-verified topological execution, and task-triggered, evidence-gated human--robot memory co-updating. The robot has no prior on-site traversal or site-specific training. Deployment requires only a floor plan, place clues, and an initial topology-odometry alignment. Visual depth point-goal navigation leverages verified node arrivals to perform translational re-anchoring, without requiring a robot-constructed global SLAM map. Human--robot memory co-updating fuses complementary sources of information. Human operators specify task constraints that are unobservable via vision and report offline environmental modifications, whereas the robot provides physical-state evidence through active in-situ validation. Task relevance governs whether human-reported environmental changes undergo robot validation during the active mission. Field-collected perceptual evidence gates memory updates, maintaining an explicit separation between human-specified permissions and robot-observed traversability. Our experiments on a Unitree Go2 show that AVALON with X-NavDP completes a 12.81 m Intersection-to-Dining mission in 46.4 s (5/5) with no mapping, 40.9% less robot time than DimOS-Go2 (78.5 s, 4/5), while policy-only X-NavDP times out in all of the 5 runs. These demonstrate the superiority of the proposed topological navigation framework.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.21586 [cs.RO] |
| (or arXiv:2610.21586v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21586 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Guiyin Tian [view email]
[v1]
Thu, 15 Oct 2026 09:33:04 UTC (30,410 KB)
来源：arXiv cs.AI · arxiv.org