Policy for Unitree Go2

[SMEAC]

Does anyone have a well trained policy for the Unitree Go2?

I am hoping to use the Unitree Go2, in a similar extension to that of the Quadruped extension in the Robotics examples, for the Spot Robot from Boston Dynamics. This robot comes pre-trained and you can start working with it straight away. But for the Unitree Go2 I have to run through the reinforcement training tutorials, to get a policy, and regardless of how long I train or how I fine tune the parameters, I have never been completely happy with the results.
Noting that the kinematic training isn’t even my area of work, I just seem to be wasting hours trying to get a start point for a well known and often used robot.

Many thanks in advance

[baryn]

Hi,
In this package a model trained with PPO on rough terrain for Go2 is provided. model_7850.pt.
I have not tested it yet. The package uses IsaacLab but I guess it might be possible to create a UnitreeGo2RoughTerrainPolicy inspired from IsaacSim SpotFlatTerrainPolicy that loads a model like so by replacing the current model with model_7850.pt:

self.load_policy(
    assets_root_path + "/Isaac/Samples/Policies/Spot_Policies/spot_policy.pt",
    assets_root_path + "/Isaac/Samples/Policies/Spot_Policies/spot_env.yaml",
)

Let me know if you succeed in applying this approach.

Cheers
Bryan