Introduction
Training data needs enough context to interpret each trajectory. Record which robot collected it, which sensors were active and how actions are represented. Identify the task, its outcome and the conditions for reuse in training or evaluation.
A dataset needs a life cycle
Planning collection processing annotation fusion storage release use and retirement all create quality and security decisions. The public scope of ISO/CD 26264-1 describes a dataset life cycle; the committee draft is still under development. That framing matters because a clean training file can still be unsafe to release or impossible to audit later if its origin was not recorded.
Embodiment metadata changes what the data means
The same wrist motion can produce different physical behavior on two robots with different arm lengths joint limits hands or control modes. Dataset records should capture embodiment configuration calibration sensor setup action representation and timing. Otherwise a model can learn from data whose physical context is missing.
Benchmark data should preserve failures
Removing failed attempts makes training data look clean but weakens evaluation. Failures show ambiguous commands bad grasps occlusion contact mistakes and recovery behavior. A mature dataset can label why an episode failed and whether the operator robot or environment caused the break.
Limitations and missing information
- No interoperability trial, dataset audit or normative clause-level assessment is supplied here.
Sources and methodology
The public records were checked on 29 September 2026. ISO/CD 26264-1 is a committee draft, and SAMR 20253226-T-604 is a national standardisation project covering real, simulated and synthetic data. Their published scopes are not finalized file-format specifications.
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