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Unable to reproduce the performance of the pretrained checkpoint for Calvin-Sim #6
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And qualitatively, the model also seems to move more erratically compared to the pretrained model. combined_test.mov |
Hi Ping-C, Can you un-comment line 198 of |
It worked! Kevin, you are amazing! |
Hello Ping-C, but the result is not good : I noticed that you have a high success rate in evaluating pre-trained models, so I wanted to ask if there are any other operations you perform during the evaluation besides downloading the model and modifying the paths? The actions of the robotic arm seem strange in some tasks, and I suspect that it may be an issue with GCBC.The robotic arm has even moved outside the field of view. |
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@houyaokun Hi, have you solved the problem? I am struggling with the same issue. 😭 |
yeah, |
@houyaokun Thank you for your quick reply. 😄 Actually, I am having problem with reproduction. My results are so bad with the provided pre-trained models. How did you handle this problem? Thank you so much for your reply. I am struggling with this about a week. |
@Ping-C Hi, I am struggling with the performance. I did all things same as they told me to do. But the performance is bad on provided pre-trained models. How did you reproduce? Is there anything else I need to do besides the default settings? |
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So the problem is domain gap with dataset. I will take a closer look at the rendering part. |
Hello Kevin,
I am back again. Thank you for looking at this issue!
So I attempted to reproduce the performance of the pretrained goal conditioned policy, but was unable to reproduce the performance of your pretrained checkpoint in Calvin-sim, and was wondering whether you could potential shed some lights on what I may be missing. The answer can be short, and doesn't have to be complete. Some simple pointers would likely suffice.
First, I downloaded the calvin-sim data, and preprocess it with
experiments/susie/calvin/data_conversion_scripts/goal_conditioned.py
on the abc training + d validation dataset. To get it working, I had to modify theraw_dataset_path
,tfrecord_dataset_path
, and then comment out the following section of codeSecond, I then trained the goal conditioned policy on calvin-sim using the following script
after updating
data_path
,save_dir
inbridge_data_v2/experiments/susie/calvin/configs/gcbc_train_config.py
.I trained the model for 2 million steps as specified in the config, and the loss level went from ~2.5 to roughly 0.65 at the end of the training (see the plot below). Note that I did have to resume the checkpoints multiple times throughout. I then ran evaluations on multiple checkpoints throughout training coupled with the pretrained diffusion model, and these are roughly the success rate that I got for the each no. of instruction chained.
which is much worse than your pretrained gc policy + your pretrained diffusion model
If you could potentially give me some pointers on what I may be doing incorrectly, it would be greatly appreciated! :)
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