- Signal alignmentStates, actions, vision, and timing have to line up cleanly.
- Task coverageThe dataset needs both success and meaningful failure diversity.
- ReusabilityMetadata, manifests, and consistent schemas matter if you want long-term value.
Robot data quality is what turns demos into durable learning infrastructure
Good data is not just more data. It is aligned, reproducible, task-aware, and ready to support replay, benchmarking, and retraining.
Higher-quality data reduces retraining waste, improves regression confidence, and makes teams more willing to scale hardware programs.