update

Author: liruiw

.gitignore

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+*.pyc
+.idea/*
+*.so
+*egg-info*
+*.avi
+*.pdf
+output
+runs
+experiments/runs
+misc
+data
+output_misc
+*.obj
+*.xyz
+*.json
+*.zip
+data_2
+ngc

.gitmodules

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+[submodule "OMG"]
+	path = OMG
+	url = https://github.com/liruiw/OMG-Planner.git

LICENSE

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+MIT License
+
+Copyright (c) 2020  
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

OMG

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+# GA-DDPG
+
+[[website](https://sites.google.com/view/gaddpg), [paper](https://arxiv.org/abs/2010.00824)]
+
+![image](assets/gaddpg.gif)
+
+
+### Installation
+
+0. Setup: Ubuntu 16.04 or above, CUDA 10.0 or above, python 2.7 / 3.6
+
+1. * (Required for Training) - Install [OMG](https://github.com/liruiw/OMG-Planner) submodule and reuse conda environment.
+   * (Demo) - Install GA-DDPG inside a new conda environment
+	    ```angular2html
+	    conda create --name gaddpg python=3.6.9
+	    conda activate gaddpg
+	    pip install -r requirements.txt
+	    ```
+2. Install [PointNet++](https://github.com/liruiw/Pointnet2_PyTorch)
+
+3. Download environment data with ```bash experiments/scripts/download_data.sh ```
+
+
+### Pretrained Model Demo
+0. Download pretrained models with ```bash experiments/scripts/download_model.sh ```
+1. Demo model test ```bash experiments/scripts/test_demo.sh```
+
+Example 1      |  Example 2
+:-------------------------:|:-------------------------:
+<img src="assets/demo.gif" width="224" height="224"/>  |  <img src="assets/demo3.gif" width="224" height="224"/>
+
+### Save Data and Offline Training
+0. Download example offline data with ```bash experiments/scripts/download_offline_data.sh ``` The .npz dataset would be saved in data/offline_data and can be loaded for training.
+1. To save extra gpus for online rollouts, use the offline training script ```bash ./experiments/scripts/train_offline.sh bc_aux_dagger.yaml BC```
+2. Saving dataset ```bash ./experiments/scripts/train_online_save_buffer.sh bc_save_data.yaml BC```.
+
+### Online Training and Testing
+0. We use [ray](https://github.com/ray-project/ray) for parallel rollout and training. The training scripts might require adjustment according to the local machine. See ```config.py``` for some notes.
+1. Training online ```bash ./experiments/scripts/train_online_visdom.sh td3_critic_aux_policy_aux.yaml DDPG```. Use visdom and tensorboard to monitor.
+2. Testing on YCB objects with ```bash ./experiments/scripts/test_ycb.sh demo_model```. Replace demo_model with trained model. Logs and videos would be saved to ```output_misc```
+
+
+### Note
+0. Checkout ```core/test_realworld_ros_final.py``` for an example of real-world usages.
+1. Related Works ([OMG](https://github.com/liruiw/OMG-Planner), [ACRONYM](https://github.com/NVlabs/acronym), [6DGraspNet](https://github.com/NVlabs/6dof-graspnet), [6DGraspNet-Pytorch](https://github.com/jsll/pytorch_6dof-graspnet), [ContactGraspNet](https://github.com/NVlabs/contact_graspnet))
+2. Please use Github issue tracker to report bugs. For other questions please contact [Lirui Wang](mailto:[email protected]).
+
+
+### File Structure
+```angular2html
+├── ...
+├── GADDPG
+|   |── data 		# training data
+|   |   |── grasps 		# grasps from the ACRONYM dataset
+|   |   |── objects 		# object meshes, sdf, urdf, etc
+|   |   |── robots 		# robot meshes, urdf, etc
+|   |   └── gaddpg_scenes	 	# test scenes
+|   |── env 		# environment-related code
+|   |   |── panda_scene 		# environment and task
+|   |   └── panda_gripper_hand_camera 		# franka panda with gripper and camera
+|   |── OMG 		# expert planner submodule
+|   |── experiments 		# experiment scripts
+|   |   |── config 		# hyperparameters for training, testing and environment
+|   |   |── scripts 		# main running scripts
+|   |   |── model_spec 		# network architecture spec
+|   |   |── cfgs 		# experiment config and hyperparameters
+|   |   └── object_index 		# object indexes
+|   |── core 		# agents and learning
+|   |   |──  train_online 		# online training
+|   |   |──  train_test_offline 	# testing and offline training
+|   |   |──  network 		# network architecture
+|   |   |──  agent 		# main agent code
+|   |   |──  replay_memory 		# replay buffer
+|   |   |──  trainer 	# ray-related training setup
+|   |   └── ...
+|   |── real_world		# real world experiment scripts
+|   |── output 		# trained model
+|   |── output_misc 	# log and videos
+|   └── ...
+└── ...
+```
+
+### Citation
+If you find GA-DDPG useful in your research, please consider citing:
+```
+@inproceedings{wang2020goal,
+	author    = {Lirui Wang, Yu Xiang, Wei Yang, Arsalan Mousavian, and Dieter Fox},
+	title     = {Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds},
+	booktitle = {arXiv:2010.00824},
+	year      = {2020}
+}
+```
+
+## License
+The GA-DDPG is licensed under the [MIT License](LICENSE).

README.md

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+ 
+