Charbel Abi Hana, Rayan Zaitouni, Kameel Amareen, Mohamad Mostafa, Hasan Sayour, Wei Liu, Yan Chang, Mihir Acharya, Anthony Rizk · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.28432 · cited by 0 works (OpenAlex) · open-access PDF
经典的Cosworld航行速度增加的机器人导航集成数据是可靠的,但通常需要在每个新的机器人类型和地点上重新设计。基于学习的导航将这种努力从手动调节的参数转移到数据生成和培训,其中一个政策通过模拟精确调节对每个新的实施或设施进行调整。对于真正的工业机器人,这些政策是否需要可靠地重新设计。我们研究了NVIDIA的X-Mobility和COMPASS工作流程,以便在IdealWorks机器人上培训学习的导航政策。在模拟中,我们将COMPASS基准扩展到两种实施方案中,在数字上调节的差异驱动IV$\mkern-1.5mu.mkern-1.5mu.$上保持可靠性。我们通过
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 embodiments, the differential-drive iw$\mkern-1.5mu.\mkern-1.5mu$hub and an omnidirectional Humanoid Mobile Platform (HMP), and with a digital twin of the Idealworks R&D center. For the real world, we fine-tune X-Mobility on data from this digital twin, with and without sim-to-real variants generated by NVIDIA Cosmos, train COMPASS on top of it from scratch in the digital twin, deploy the resulting policies on the iw$\mkern-1.5mu.\mkern-1.5mu$hub's onboard Jetson AGX Xavier, and benchmark them in the Idealworks R&D center against COMPASS trained only on NVIDIA's environments and the existing Idealworks navigation stack on task success, efficiency, safety, smoothness and onboard compute. Running every static scenario both in a digital-twin replica and in the real facility measures the sim-to-real gap and how far Cosmos-augmented data closes it. Training in the digital twin with Cosmos sim-to-real augmentation raises the real-world success rate of learned navigation from 65.3% to 91.7% across four scenarios and nearly halves its weighted travel time compared with simulation-only training, bringing it within 8.3 points of the production navigation stack, fully onboard; with an integrated global planner, it also reaches far, occluded goals in every trial.
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Topics: Compass, Benchmark (surveying), Benchmarking, Computer science, Robot, Task (project management)
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