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In the Go2 crate climbing task, I observed that the success rate is much lower than the 0.9 mentioned in the paper. To address this, I attempted to adjust the height of the crate and modified the reward parameters as follows:
Humanoid Crate Pushing Reward Table
Gait: 5.0
Upright: 0.01
Yaw: 0.1
Velocity: 1.0
Torso Height: 0.5
Energy: 0.01
Contact Reward: 0.05
However, the Go2 robot only succeeded 1 or 2 times out of 10 attempts with an unstable posture. How can I further revise the code to improve the success rate of this climbing task?
The text was updated successfully, but these errors were encountered:
@baijubaiju233 thanks for the discovery - some recent package updates might have affect the results.
without changing the rewards, can you try playing with the parameters in dial_mpc/examples/unitree_go2_crate_climb.yaml? Some settings that might be helpful to tune are:
Hsample: horizon length Hnode: number of spline node. must be an integer factor of Hsample Ndiffuse: number of diffusion steps traj_diffuse_factor: variance schedule at every diffusion step
For example, increasing Ndiffuse to 3 and traj_diffuse_factor to 0.9 yields resonable results on my side.
Feel free to play around and find a good number to put in a PR! Thank you.
In the Go2 crate climbing task, I observed that the success rate is much lower than the 0.9 mentioned in the paper. To address this, I attempted to adjust the height of the crate and modified the reward parameters as follows:
Humanoid Crate Pushing Reward Table
Gait: 5.0
Upright: 0.01
Yaw: 0.1
Velocity: 1.0
Torso Height: 0.5
Energy: 0.01
Contact Reward: 0.05
However, the Go2 robot only succeeded 1 or 2 times out of 10 attempts with an unstable posture. How can I further revise the code to improve the success rate of this climbing task?
The text was updated successfully, but these errors were encountered: