Industrial Workplace Egocentric FHD Samples Dataset
MIT-licensed industrial first-person video samples
A small MIT-licensed set of industrial and workplace egocentric video samples for robotics, VLA, imitation learning, and manipulation demos.
Industrial manipulation examples
Useful for examples and smoke tests; scale is intentionally small.
Inspect schema and run a bounded sample audit before committing to the full release.
Has observation, action/state proxy, and task or language context.
fit 85 · confidence 55
Has rich observation and semantic context, but limited geometry/sim-real alignment.
fit 68 · confidence 55
Contains observation, intent, and action/state, but feedback/correction signal is weak.
fit 72 · confidence 55
Verified facts and provenance
Claims, metadata verification, and sample verification are shown separately.
Unknown — no machine-readable schema facts have been captured.
Unknown — metadata conclusions do not prove sample coverage, alignment, or file integrity.
Declared loop signal coverage
Signals inferred from official metadata; Data pipeline verification is still pending.
Observation / ego video
video · Open workplace-video ingestion tests · MIT-licensed industrial first-person video samples · A small MIT-licensed set of industrial and workplace egocentric video samples for robotics, VLA, imitation learning, and manipulation demos.
Action / hand pose / robot state
Industrial manipulation examples · A small MIT-licensed set of industrial and workplace egocentric video samples for robotics, VLA, imitation learning, and manipulation demos.
Gaze / attention
No decision-grade evidence captured yet.
Language intent / task phase
industrial tasks · workplace labels
Feedback / correction / failure
No decision-grade evidence captured yet.
Sim-real pairing
No decision-grade evidence captured yet.
License / format / access
Open · MIT · video · text metadata
Model and task fit · OpenBot inference
Has observation, action/state proxy, and task or language context.
fit 85 · confidence 55
Has rich observation and semantic context, but limited geometry/sim-real alignment.
fit 68 · confidence 55
Contains observation, intent, and action/state, but feedback/correction signal is weak.
fit 72 · confidence 55
Failure, correction, intervention, and recovery annotations have not been verified.
fit 50 · confidence 15
Good tasks
Blockers and unresolved evidence
- Gaze / attentionunknownNot enough evidence to classify this signal. Verify metadata or a bounded sample.
- Feedback / correction / failureunknownNot enough evidence to classify this signal. Verify metadata or a bounded sample.
- Sim-real pairingunknownNot enough evidence to classify this signal. Verify metadata or a bounded sample.
Raw dataset signals
OpenBot fit
- Industrial manipulation examples
- Open workplace-video ingestion tests
- Domain-specific dataset search demos
Integration notes
- Useful for examples and smoke tests; scale is intentionally small.
- MIT license makes it easier to reference in open demos.