aiqbench analysis
This research presents UNI, a novel robot-free data collection framework that leverages low-cost rollators and smartphones to generate high-quality supervision for wheeled mobile robots. By utilizing physical constraints inherent to the rollator, the method effectively biases demonstrations toward feasible navigation paths while significantly reducing trajectory prediction errors in visual-navigation models. The study validates this approach through closed-loop transfer learning to powered wheelchairs, demonstrating a practical pathway to scaling real-world robot training data without expensive teleoperation.
Universal Navigation Interface: Robot-Free Data for Wheeled Robot Navigation
arXiv:2609.20114v1 Announce Type: new 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.
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.
8 pages, 4 figures, 7 tables. Project page: this https URL
Robotics (cs.RO)
arXiv:2609.20114 [cs.RO]
(or arXiv:2609.20114v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.20114 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 251 words
