Collect
Build scalable channels for manipulation-rich first-person activity data.
Human video for robot learning
Human video for robot learning.
Robot learning is bottlenecked by expensive teleoperation. Markov Intelligence turns ordinary first-person video into the tasks, objects, hands, and context robots need to learn from the real world.
Why egocentric video
Teleoperation is precise. Human activity is abundant.
First-person human video can scale across homes, workplaces, tools, objects, and long-tail tasks without putting a robot in every loop.
The challenge is transfer: extracting task structure, hand trajectories, object affordances, intent, and state changes in a form that improves downstream robot policies.
Structure
Turn raw video into task segments, object tracks, hand motion, narration, and environment state.
Transfer
Measure when human egocentric data improves robot perception, planning, and policy learning.
From human activity to robot capability