Charbel Abi Hana, Rayan Zaitouni, Kameel Amareen, Mohamad Mostafa, Hasan Sayour, Wei Liu, Yan Chang, Mihir Acharya · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28432 · open-access PDF
Las pilas de navegación robótica clásicas son fiables, pero a menudo requieren reingeniería para cada nuevo tipo de robot y sitio. La navegación basada en el aprendizaje cambia este esfuerzo de parámetros ajustados a mano a la generación de datos y capacitación, donde una política se adapta a cada nueva implementación o instalación mediante el ajuste en simulación. Si las políticas adaptadas de esta manera se mantienen fiables en robots industriales reales ha visto poca prueba sistemática.
Machine translation (Meta M2M100); the English original follows.
Classical robot navigation stacks are reliable but often require re-engineering for each new robot type and site. Learning-based navigation shifts this effort from hand-tuned parameters to data generation and training, where a policy adapts to each new embodiment or facility by fine-tuning in simulation. Whether policies adapted this way stay reliable on real industrial robots has seen little systematic testing. We study this for NVIDIA's X-Mobility and COMPASS workflow for training learned navigation policies on Idealworks robots. In simulation, we extend the COMPASS benchmark with two embodi
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