Robot developers do not have one compute problem. They have 3. A policy is trained on GB200 or H100 clusters, tested in Isaac Sim on RTX GPUs, then validated on a Jetson mounted inside a real robot. Each tier has its own cluster, its own scheduler, and its own glue scripts. NVIDIA OSMO is NVIDIA’s answer to that fragmentation: an open-source, Kubernetes-native workflow orchestrator that lets a team describe the whole pipeline in a single YAML file and run it across every tier without touching infrastructure code. Deployable? Yes. OSMO is Apache-2.0 licensed , ships Helm charts and containers on NGC , and has a local quickstart that runs the full control plane on a workstation with KIND. The Three Computer Problem NVIDIA frames physical AI as a three computer problem . Training happens on data-center GPUs. Simulation, physics, and sensor rendering happen on workstation-class RTX hardware. Deployment and hardware-in-the-loop (HIL) testing happen on edge devices such as Jetson AGX Thor, usually on premises. Each tier usually gets its own tooling, and the handoffs are where custom scripts accumulate. OSMO treats all 3 as backends of one control plane. Each backend is a Kubernetes cluster registered through the CLI. Workflows never name a cluster. They name a platform (for example gb200 , rtx-pro-6000 , or jetson-agx-thor ) and OSMO routes the task to a pool that offers it. What a Workflow Looks Liake The Repo’s canonical example is 3 tasks cAhained by data:A simulation runs an Isaac Sim container on rtx-pro-6000 train-policy runs a PyTorch container on gb200 with 8 GPUs, taking
Source: MarkTechPost