γ₀: A Generalist Policy for Multi-Embodiment Motion Control

We’re building a foundation motion policy for any robot, on the ground or in the air, with wheels, legs or rotors.

It learns across millions of embodiments derived from a growing collection of more than 200 realistic robot models. With every robot added, the policy becomes more capable and better able to transfer to new robots.

Contribute your robot and help us build the largest open-source robot collection for multi-embodiment training and deployment! ↓

Open the interactive playground

Real-world deployments

A single policy controls every robot at the joint level.

Unitree Go2 · Silver Badger · Unitree G1

Unitree H1

Training methodology

We take Unified Robot Morphology Architecture (URMA) and Embodiment Scaling Laws to the extreme by randomizing robot embodiments on the fly throughout training. This exposes a single policy, conditioned on each embodiment, to the broadest possible range of robot morphologies. By learning from this diversity, the policy develops generalizable motion skills that transfer to new embodiments.

Our robot collection

6 of 308 robots

1X Neo

Adam Lite

Agibot A2

AgiBot A3 T2.5

Agibot D1

Agibot G1

Contribute to γ₀

Help us build a foundation motion policy that transfers across robots on the ground and in the air, with wheels, legs or rotors, supported by the largest open-source robot collection.

We have two ways for you to get involved!

Contact us at contribute@gamma-zero.com until October 15.

We’ll publish the policy and robot collection shortly after.

A.

Contribute your robot model

Share a valid URDF for a real-world robot that we can simulate with MuJoCo, including meshes, inertial parameters, joint limits, and actuator torque and velocity limits. Accepted models will become part of the open-source collection, and contributors will be cited or acknowledged. We welcome legged robots, wheeled robots, manipulators and drones. Standalone robot hands are outside the scope of this collection.

B.

Train and deploy with us

Collaborate with us to extend the training pipeline and deploy γ₀ on new hardware. Contributions spanning both training and real-world deployment may qualify for authorship.