Markov Robotics

“Too late to explore earth, born too early to explore space” maybe not. Scaling humanity beyond the comfort of planet Earth requires abundant physical automation, a.k.a. robots. We are building towards that goal, with our first act being solving zero-shot general-purpose dexterity. This will allow us to deploy robots that act as economically valuable physical agents.

At the core of our approach is a world model in the truest sense: by conditioning on high-fidelity modalities such as pressure and proprioception, the policy develops an extremely rich internal spatiotemporal representation of the world. Deployed with efficient inference, this world-model policy enables closed-loop control and prevents errors and hallucinations from accumulating. This is what makes it a powerful predictor of how the world evolves over time.

Our path scales on both sides of the system. Upstream, humanoid embodiment lets us learn directly from humans and the vast video record of the world. Downstream, building the policy that can run across humanoid platforms lets us deploy through partners with great hardware and existing distribution. Every deployment creates more real-world experience, which makes the policy better, which makes deployment more valuable. This is the data-distribution flywheel we are building.