Robotics warehouse video action labeling.
Challenge
Training manipulation policies for warehouse robots requires precisely segmented action sequences from video: start and end frames, success versus failure, and clean exclusion of invalid frames.
Approach
io-ai labeled the full pick-and-pack action vocabulary: picking_object (success or failure by grip outcome), object_to_bag, adjusting_bag and bag_to_drop, with strict start/end definitions (for example, start when the light begins to move; include waiting time where specified).
Explicit do-not-label rules covered blur, freezes, human interventions and cleanup, with one object tracked per annotation instance.
Quality
Every task ran through io-ai's multi-level QA pipeline, with first-batch audits before scale-up and full rejection-reason metadata on every item, holding over 98% accuracy on rolling audits, with weekly status reporting.
Result
Clean, temporally precise action data for robotic manipulation training, with unusable video blocked rather than guessed.
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