NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing

Here's how it works. 👇

1. One YAML, three computers

Robot pipelines span 3 compute tiers: training on GB200/H100, simulation on RTX PRO 6000, hardware-in-the-loop on Jetson AGX Thor. OSMO treats all 3 as backends of one control plane.

→ Tasks name a platform (gb200, rtx-pro-6000, jetson-agx-thor), never a cluster

2. Dependencies are data

The README example chains 3 tasks: Isaac Sim → PyTorch training with 8 GPUs → ROS eval on Jetson, writing results to a named dataset.

→ Placement from platform, ordering from inputs, persistence from outputs

3. Same file, laptop to cloud

Runs on KIND locally and on EKS, AKS, GKE, on-prem, or air-gapped clusters. Release 6.3.0 added a multi-provider deploy script with MinIO, Azure Blob, or S3 storage.

→ Zero code changes between environments


4. Built for production

NVIDIA KAI Scheduler by default, NVLink topology-aware placement, per-group timeouts, RBAC sidecar, OAuth2 login, TLS at the gateway, cloud workload identity.

→ Powers Project GR00T, Isaac Lab, Isaac Sim, and Isaac ROS internally

Full analysis: https://www.marktechpost.com/2026/09/14/nvidia-open-sources-osmo-one-yaml-orchestrates-physical-ai-training-simulation-and-robot-testing/

Repo: https://github.com/NVIDIA/OSMO