Comments (2)
I want to use amp training to walk on complex terrain. Have you tried it before? What parameters do you think have a significant impact on terrain adaptability
Do you have any ideas? I am also trying to train walking//running on terrain. I wonder the composition of the task reward and the balance between task reward and style reward.
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I want to use amp training to walk on complex terrain. Have you tried it before? What parameters do you think have a significant impact on terrain adaptability
Do you have any ideas? I am also trying to train walking//running on terrain. I wonder the composition of the task reward and the balance between task reward and style reward.
This balance is difficult to quantify, and I believe it is related to many factors. Therefore, during training, I continuously increased the style reward from scratch, and made choices through multiple experimental results. I also found that excessive style rewards can affect the robot's terrain robustness and speed following tasks .
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Related Issues (16)
- ValueError: Expected parameter loc (Tensor of shape (32880, 12)) of distribution Normal(loc: torch.Size([32880, 12]), scale: torch.Size([32880, 12])) to satisfy the constraint Real(), but found invalid values: HOT 11
- RuntimeError: CUDA error: an illegal memory access was encountered HOT 1
- ERROR:CHANGE GO1 HOT 1
- Deployment code HOT 1
- Problem to work with another quadruped robot HOT 1
- RuntimeError: vstack expects a non-empty TensorList HOT 1
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