Open egocentric releases moved fast. Roundups now compare corpora from industrial scale dumps to kitchen benchmarks and dexterous hand sets. That abundance is useful. It is not a substitute for every production brief.
What open datasets are good for
Pretraining experiments, paper baselines, and learning your training stack. They teach the field what scale and viewpoint look like. They rarely match your exact objects, fixtures, or commercial constraints.
What custom collection is for
Proprietary skills, NDA environments, exclusive rights, sensor suites you actually deploy, and packaging into LeRobot-compatible or other train formats. Enterprise buyers also need validation, versioning, and a named delivery process.
A practical rule
Start with open data if you are proving a method. Switch to custom when model errors cluster on your products, scenes, or embodiment. That is the robotics data bottleneck most startups hit after the first demo. Custom robotics datasets are built for that stage.