jstmn / ikflow Goto Github PK

Thank you for your excellent work. If I want to train my own robot, what do I need to prepare

1. Add your own robot's URDF file
2. Modify the code to include it in robots. py
3. Modify the model_ Descriptions.yaml file
4. Generate Dataset
5. Training Model
6. Validate the model
   I have tried the above steps, but there are still many things that seem incorrect. Can you give me some guidance?

I have a question about the negative logarithmic likelihood function during code training. I found that the negative logarithmic likelihood function I was training had negative values.

Is this caused by my lack of normalization in processing the dataset? I did not modify the code for ikflow.

And when I looked at the code, I found that the
neg_log_likeli is defined by this code：

output, jac = self.nn_model.forward(x, c=conditional, jac=True)
zz = torch.sum(output**2, dim=1)
neg_log_likeli = 0.5 * zz - jac
loss = torch.mean(neg_log_likeli)

I didn't see the logarithmic operation for zz and jac here when watching Graph INN, nor did I see the softmax layer in the final output of the model. I have been troubled here for a long time, and I really hope to receive your explanation and assistance from the author.