Questions I’m trying to untangle, things I’ve tried, and what I think now—mostly across physical AI, simulation, digital twins, and developer tools.
I would design MES around shared operational state, physical context and decision services. The test is whether another manufacturing function can reuse an assessment and remain consistent when the operation changes.
A finding needs the right next action. These 3D deliveries taught me to separate defects and missed requirements from scope gaps, missing evidence and evaluator problems before assigning a repair. A requested variation may need no repair at all.
I built SplatStage around editing, export and composition workflows. Starting again, I would test delegating that production job to a runtime agent. The question is which development work that avoids, and which tools, checks and user-facing features I would still maintain.
Five Astra probes produced no hallucination example. One correctly left contact, mass and friction unresolved. Together with a published object-grounding failure, that result shows why spatial evaluation needs supported, contradicted and unknown outcomes.
A digital twin needs a surrogate only when repeated prediction creates value and the solver misses that decision's clock. Ventilation makes the case; fast energy models show why the rule is not universal. Part 1 of a five-part series.
Years of MES work taught me where automation stops: at tasks whose decisive state cannot be written down or seen. Tactile input earns its place when it reveals that missing state; contact alone is not enough.
Every digital twin needs a usable digital starting point. CAD, LiDAR, photogrammetry and Gaussian splatting preserve different kinds of truth, so the representation should follow the decision. Part 1 of a three-part series.
A refinery valve operation shows why robot capture should begin with the task: preserve the motion, contact, timing and measurements the policy will need.
Robot foundation models are getting better at handling situations they were not shown. That makes the old commissioning questions more important: where is the model valid, how do I know it has left that range, and what happens next?
A robot can finish the task and still behave unsafely along the way. That distinction makes this benchmark worth reading.
I choose hardware by the work it runs, the memory it needs and what has to move between devices. A SplatStage run shows why the GPU model alone was a poor guide to sizing the machine.
Before changing model weights, I would identify the gap: missing domain knowledge, unreliable workflow behavior or a deployment constraint. A pharma recipe project, an agent workflow and an edge counterexample lead to different first interventions.
MES was never really about running the machines. It was about improving the operation, and it was limited by how much had to be modeled by hand and decided by people. Physical and agentic AI can change that, but only if the plant's systems are joined through shared operational context.
The useful signal is not that world models are solved. It is that investors and research leaders increasingly see predictive models of physical dynamics as a distinct bottleneck worth funding.
A pretrained robot policy can supply useful action skills. An automotive parts-kitting cell still needs a way to adapt those skills, verify each kit and respond when parts or conditions change. That is the pipeline I would build now.
A gas-leak prototype showed me the cost of building inside Kit. Standalone Omniverse libraries make the application framework a choice, including for some interactive tools.
Production use no longer requires an NVIDIA AI Enterprise subscription. The useful distinction is between permission to deploy, the license terms that still apply and the support your team needs.
A request to scope a robot training center became a five-stage working model. I use one task to explain the handoffs, the tool choices and what still needs testing before pipeline reuse becomes a business or deployment claim.
From the industrial systems I know, rendering is only one part of Omniverse. The harder job is connecting source data, giving teams a shared context, and reusing it for visibility, simulation, optimization and Physical AI.