// case_studies
How we approach physical AI data problems.
The formats below show how Datafy Lab structures an engagement end to end.
Sample case-study formats · not real clients · results are placeholders until projects complete
Sample case study 01
Warehouse Robot Picking Edge Cases
Challenge
A warehouse robotics team had strong demo performance but struggled with damaged packages, cluttered bins, reflective labels, and unusual object positions.
Datafy Lab approach
- Data Failure Audit
- Edge-case taxonomy
- Field capture plan
- Annotation workflow
- QA review
- Dataset certification
ResultReplace with verified metric after project completion.
Sample case study 02
Egocentric Video for Humanoid Task Learning
Challenge
A robotics team needed human POV task data for workplace task understanding, with consistent step-wise structure and failure examples.
Datafy Lab approach
- Task list design
- Consent-based POV capture
- Step-wise task labeling
- Object interaction annotation
- Failure tagging
- Dataset certificate
ResultReplace with verified metric.
Have a physical AI data problem like these?
Start with a Data Failure Audit and we’ll map the missing data your model needs next.
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