Abstract
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 this http URL 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 this http URL'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.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28432
