Collect
Build opted-in passive collection channels for manipulation-rich activity in real daily and work routines.
Passive egocentric data for robot learning
Robots need data from the real world.
Robot learning is bottlenecked by expensive teleoperation and scripted data collection. Markov Intelligence turns passively captured first-person human activity into structured signals like tasks, objects, hands, motion, and context that help robots learn from real workflows.
Why passive collection
Scripted tasks do not scale. Real activity does.
Most robot-learning datasets ask people to perform assigned tasks. That creates useful benchmarks, but it does not capture the volume, variation, and long-tail context of what people already do every day.
Markov Intelligence is building toward opted-in egocentric collection from normal routines and work: manipulation-rich activity across homes, workplaces, tools, objects, interruptions, mistakes, and real task transitions.
The hard part is transfer: converting noisy passive video into motion, affordance, intent, and state-change signals that can be grounded with smaller amounts of robot data.
Structure
Turn noisy first-person streams into task segments, object tracks, hand and wrist motion, narration, and environment state.
Ground
Align human egocentric data with robot demonstrations to measure when it improves downstream policies.
From real routines to robot capability