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@ipa-mjp Did you run this command line > sudo apt-get install ros-kinetic-universal-robot

@YUHANG-Ma I am having the same GPU 3060 as you and would like to upgrade the CUDA version in the docker to test this repo. Do you mind to tell...

In my case, I can access the GPU from docker container right after a fresh install. The GPU error happens only after a reboot. I tried the possible methods mentioned...

However, a proper plan can be generated if I run `simulated_actions.bash`: ``` #!/bin/bash rosservice call /rosplan_problem_interface/problem_generation_server; rosservice call /rosplan_planner_interface/planning_server; rosservice call /rosplan_parsing_interface/parse_plan; rosservice call /rosplan_plan_dispatcher/dispatch_plan; ``` and this is the...

I faced the same error . @gav1n-cheung Did you managed to find a solution to this? My setup: cuda=11.3 tensorflow=2.5 python=3.6

Found a solution to train without issue. I added the parameter `save_weights_only=True` in the modelcheckpoint() > keras.callbacks.ModelCheckpoint('./logs/{}/model/weights.ckpt'.format(config['log_dir']), 'val_sparse_categorical_accuracy', save_weights_only=True, save_best_only=True)

Hi logan, Your results looks great. May I know what is the dataset size and how long do you train the model? Chan

@loganbruns From your convert_gqn.py I can see that you saved each scene with N number of frames as one TFrecord. As you mentioned you trained the model with 15k training...

Thank you for your quick reply. I have checked the my custom dataset with the `gqn_dataset.ipynb` and the jupyter notebook is able to display the scene frames and camera positions...

Hi @torbsorb , Thank you for the reply. My main intention is to increase the "certainty" in knowing when the capturing process is done or failed. Currently, there is no...