Problem
Research questions and friction points this paper is trying to address.
navigation data
mobile robots
data collection
teleoperation
wheeled robot
Universal Navigation Interface: Robot-Free Data for Wheeled Robot Navigation
🤖 AI Summary
研究提出Universal Navigation Interface(UNI)，利用助行器和智能手机收集适合轮式机器人导航的数据，降低了数据收集成本，并通过该方法提高了视觉导航模型的准确性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Universal Navigation Interface
robot-free data collection
wheeled-feasible routes
goal-conditioned navigation models
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