To solve the data problem, the next generation of robots must learn more from less. At Markov, we are building an intelligence for any dexterous task on any robot, using orders of magnitude less data.

Our policy is a generalist, with zero-shot mastery of objects and environments it has never seen, handling force, precision and tactile tasks. It is reliable enough to deploy, fast enough to keep pace with people, and affordable to run at scale.

Most significantly, its efficiency makes cross-embodiment easy: already five end-effectors and three robot bodies on a single checkpoint.

Over 10 months, our research has unlocked entirely new scaling laws, making possible an exponential data flywheel—exactly the kind of scaling required to make robots useful, sooner.

All our demos: less than 30h of data total. 1x and autonomous.