通用导航接口：轮式机器人导航的无机器人数据
Universal Navigation Interface: Robot-Free Data for Wheeled Robot Navigation
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中文总结 AI 辅助
提出无机器人的数据收集范式UNI，利用助行器和手机收集物理约束演示，训练轮式导航模型，降低轨迹误差并实现闭环迁移。
中文摘要 AI 辅助
为移动机器人收集真实世界的导航数据通常需要特定平台的远程操作，这使得大规模数据收集成本高昂且难以扩展。我们引入了通用导航接口（UNI），一种无机器人的数据收集范式，利用四轮助行器（rollator）和智能手机来收集受物理约束的人类演示。由于助行器无法爬楼梯、通过未修剪的路缘或穿过狭窄间隙，演示自然偏向于轮式可行的路线。使用UNI，我们收集了37.2公里的真实世界导航数据，并恢复了度量轨迹，直接监督目标条件导航模型。在UNI上微调视觉导航模型，在保留的UNI演示上将轨迹预测误差降低了17.4%至24.8%。在其他导航数据集上的评估显示，收益因数据集和度量而异。我们进一步展示了在路缘、楼梯和路缘坡道场景中向电动轮椅的闭环迁移。这些结果支持低成本物理代理作为无需目标机器人即可获得的导航监督的实际来源。
英文摘要
Collecting real-world navigation data for mobile robots typically requires platform-specific teleoperation, making large-scale collection expensive and difficult to scale. We introduce Universal Navigation Interface (UNI), a robot-free data collection paradigm that uses a four-wheeled rollator walker (rollator) and smartphone to collect physically constrained human demonstrations. Because the rollator cannot climb stairs, negotiate uncut curbs, or pass through narrow gaps, demonstrations are naturally biased toward wheeled-feasible routes. Using UNI, we collect 37.2 km of real-world navigation data and recover metric trajectories that directly supervise goal-conditioned navigation models. Fine-tuning visual-navigation models on UNI reduces trajectory prediction error by 17.4-24.8% on held-out UNI demonstrations. Evaluation on other navigation datasets shows benefits that vary by dataset and metric. We further demonstrate closed-loop transfer to a powered wheelchair in curb, staircase, and curb-cut scenarios. These results support low-cost physical proxies as a practical source of navigation supervision collected without the target robot.
发表机构
- Northeastern University（东北大学）
- Institute for Experiential Robotics（体验机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。
