AVALON：零样本室内导航框架，用人类提供的拓扑记忆替代SLAM建图
AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory
机器人不建图也能进陌生建筑跑腿，靠人给的平面图和拓扑记忆，Go2 实测比基线快 40%，做导航的值得看看思路。
论文提出 AVALON，一个面向人类熟悉但机器人未到访建筑的拓扑导航框架。部署只需一张平面图、位置线索和初始拓扑-里程计对齐，无需现场遍历或建图。在 Unitree Go2 上的实验显示，AVALON 搭配 X-NavDP 完成 12.81 米的路口到餐厅任务用时 46.4 秒（5 次全部成功），比 DimOS-Go2 的 78.5 秒少 40.9%，而单独使用 X-NavDP 的 5 次运行全部超时。框架还包含人机记忆共同更新机制：人类报告视觉不可见的任务约束和环境改动，机器人通过现场验证提供物理证据。
AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory
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.
