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A dataset is built from episodes, and an episode can fail to build. Cyberwave never lets a dataset look healthy when it isn’t — the dataset is marked Failed, and each episode shows why.

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A failed episode fails the dataset

If any episode fails, the dataset is marked Failed and cannot be used to start a training. This is deliberate: training on a dataset that quietly lost part of its data produces a model you cannot trust. Open the dataset to see which episodes failed and the reason for each.

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Fixing it

Remove the failing episode from the dataset. The dataset becomes usable again straight away. If the episode’s data matters to you, record it again — or contact support if you believe the recording itself is intact.

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Exports still work

You can still export a dataset that has a small number of failed episodes — the export is built from the episodes that are valid, and tells you how many were skipped. If more than 10% of the episodes are unusable, the export stops instead of producing a badly incomplete file.

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Episodes recorded with different cameras

Episodes recorded with different cameras can be combined into one dataset, as long as each episode has the same number of cameras. The cameras are lined up by position: the first camera of every episode becomes the dataset’s first camera, and so on. If an episode is missing a camera, it is skipped rather than guessed at.

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Episodes still recording

If you try to create a dataset while one of its recordings is still being processed, Cyberwave tells you which episodes are affected and lets you choose: wait and retry, drop those episodes, or continue anyway. If you continue, those episodes may be incomplete — they will be marked failed rather than silently shortened. Waiting until processing finishes and then creating the dataset is the safe choice.

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Creating and recomputing a dataset is asynchronous

Creating a dataset (or recomputing one) returns right away — the dataset appears immediately with a Queued or Generating status. Keep polling the dataset until it reaches Ready or Failed. If the selected episodes don’t share the same number of robot or camera recordings, that also surfaces as a Failed dataset with a reason, the same way an individual episode failure does.