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.

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?

Hello

I am trying to start a training for Kuka Iiwa14. I have added the URDF file and created a subclass in jrl / robots.py. But i get this when i try to generate dataset or start a training.

Do I necessarily need the collision capsules to start a training now?

I want to add new robot to robots.py in jrl, and I have many additional links. I find that additional_link_names only accept one link, what should I do?

Hi Jeremy,

Thank you for providing the official implementation code! Some confusing points came up during applying the code and I am wondering if you can help me.

1. I am new to poetry and am using Ubuntu 20.04. When solving the dependencies, it always tried to install klampt = "0.9.1.post6" and returned error since there is no supporting version. I have no idea why this happened since it seems to me that the version is fixed to "0.9.1.post5" in the jrl project.
2. How long will the training takes for the panda robot, for the example, "python scripts/train.py --robot_name=panda --nb_nodes=12 --batch_size=128 --learning_rate=0.0005"? In the paper, it says the training is with a NVIDIA GeForce RTX 2080 Ti graphics card. For me it took several hours for one epoch, but the max epoch is set to 5000. How is that possible to finish a training process? Is there anything that I missed?

I will be greatly appreciated if I can hear from you soon.

Best regards,
Yupu

Hi, Thank you for your excellent work!

I am trying to use ikflow in my project, and my robot is different from a regular robotic arm. It has three additional floating joints, so it has a total of 9 degrees of freedom.

• I used my own script to generate the same sample and pose datasets, with shapes of [N, 9] and [N, 7], and checked the results in the simulation.
• I replaced solution_pose_errors with a simple script I implemented myself, but did not implement collision detection (I simply returned False).
• I used the following hyperparameters to train the script: --dim_latent_space=15 --nb_nodes=9 --batch_size=256 --learning_rate=0.0005, with all other parameters remaining default.

I often encounter the problem of loss being NaN around the 2nd to 5th epochs now. I saw that you also set a warning of loss being NaN in two places. Do you have any relevant suggestions?