Physical AI for Intralogistics: Benchmarking the NVIDIA Sim-to-Real Ecosystem for Cosmos-Enhanced COMPASS Navigation on Industrial Mobile Robots

Key points

• Classical robot navigation stacks are reliable but often require re-engineering for each new robot type and site.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]Physical AI for Intralogistics: Benchmarking the NVIDIA Sim-to-Real Ecosystem for Cosmos-Enhanced COMPASS Navigation on Industrial Mobile Robots
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:59 PM

  We study this for NVIDIA's X-Mobility and COMPASS workflow for training learned navigation policies on Idealworks robots.

  Classical robot navigation stacks are reliable but often require re-engineering for each new robot type and site.

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