ML pathing and collision checking

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Major effort

ML pathing and collision checking

Path generation and collision validation as a first-class workload on the hub — next to cell data, under your network boundary.

Data plane

Trajectories, meshes, cell logs, and collision labels land in stores you own. Retention and access follow plant policy.

Train and fine-tune

Versioned jobs, GPU options where you need them, and model artifacts that stay on the account.

Infer at cell pace

Path check and collision check as services your stack can call — observable, logged, and rate-aware.

Human loop

Review failed paths, label edge cases, retrain, and keep an audit trail of what changed.

  • FitSits beside robot cell software instead of a detached cloud science project.
  • IPTraining data and weights do not need a third-party tenancy to be useful.
  • GrowSame plane later hosts VLM defect work and other plant models when you are ready.
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