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Submodule .travis
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# Byte-compiled / optimized / DLL files | ||
__pycache__/ | ||
*.py[cod] | ||
*$py.class | ||
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# C extensions | ||
*.so | ||
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# Distribution / packaging | ||
.Python | ||
env/ | ||
build/ | ||
develop-eggs/ | ||
dist/ | ||
downloads/ | ||
eggs/ | ||
.eggs/ | ||
lib/ | ||
lib64/ | ||
parts/ | ||
sdist/ | ||
var/ | ||
wheels/ | ||
*.egg-info/ | ||
.installed.cfg | ||
*.egg | ||
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# PyInstaller | ||
# Usually these files are written by a python script from a template | ||
# before PyInstaller builds the exe, so as to inject date/other infos into it. | ||
*.manifest | ||
*.spec | ||
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# Installer logs | ||
pip-log.txt | ||
pip-delete-this-directory.txt | ||
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# Unit test / coverage reports | ||
htmlcov/ | ||
.tox/ | ||
.coverage | ||
.coverage.* | ||
.cache | ||
nosetests.xml | ||
coverage.xml | ||
*.cover | ||
.hypothesis/ | ||
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# Translations | ||
*.mo | ||
*.pot | ||
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# Django stuff: | ||
*.log | ||
local_settings.py | ||
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# Flask stuff: | ||
instance/ | ||
.webassets-cache | ||
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# Scrapy stuff: | ||
.scrapy | ||
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# Sphinx documentation | ||
docs/_build/ | ||
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# PyBuilder | ||
target/ | ||
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# Jupyter Notebook | ||
.ipynb_checkpoints | ||
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# pyenv | ||
.python-version | ||
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# celery beat schedule file | ||
celerybeat-schedule | ||
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# SageMath parsed files | ||
*.sage.py | ||
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# dotenv | ||
.env | ||
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# virtualenv | ||
.venv | ||
venv/ | ||
ENV/ | ||
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# Spyder project settings | ||
.spyderproject | ||
.spyproject | ||
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# Rope project settings | ||
.ropeproject | ||
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# mkdocs documentation | ||
/site | ||
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# mypy | ||
.mypy_cache/ | ||
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data/objects | ||
data/training_data | ||
data/valid/ | ||
data/sampling_data | ||
data/finetuning_data | ||
data/pretrained_model | ||
data/compressed | ||
data/models | ||
data/occlusion_dataset | ||
data/evaluation_data | ||
out/ | ||
out_viz/ |
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MIT License | ||
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Copyright (c) 2017 Shingo Kitagawa | ||
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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: | ||
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The above copyright notice and this permission notice shall be included in all | ||
copies or substantial portions of the Software. | ||
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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. |
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# Automatic dataset generation and training for dual-arm grasping | ||
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## Installation | ||
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```bash | ||
pip install --user -e . | ||
``` | ||
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## Advanced Robotics 2020: Selective dual-arm occluded grasping | ||
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### Generating datasets | ||
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Read [scripts/README.md](./scripts/README.md) | ||
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### Training OccludedGraspMaskRCNN with datasets | ||
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Read [experiments/occluded_grasp_mask_rcnn/README.md](./experiments/occluded_grasp_mask_rcnn/README.md) | ||
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## Citation | ||
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```bib | ||
@article{doi:10.1080/01691864.2020.1783352, | ||
author = {Shingo Kitagawa and Kentaro Wada and Shun Hasegawa and Kei Okada and Masayuki Inaba}, | ||
title = {Few-experiential learning system of robotic picking task with selective dual-arm grasping}, | ||
journal = {Advanced Robotics}, | ||
volume = {34}, | ||
number = {18}, | ||
pages = {1171-1189}, | ||
year = {2020}, | ||
publisher = {Taylor & Francis}, | ||
doi = {10.1080/01691864.2020.1783352}, | ||
URL = {https://doi.org/10.1080/01691864.2020.1783352}, | ||
eprint = {https://doi.org/10.1080/01691864.2020.1783352} | ||
} | ||
``` | ||
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## IROS2018: Selective dual-arm grasping | ||
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### Generating datasets | ||
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Read [scripts/README.md](./scripts/README.md) | ||
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### Training DualarmGraspFCN with datasets | ||
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Read [experiments/dualarm_grasp_fcn/README.md](./experiments/dualarm_grasp_fcn/README.md) | ||
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## Citation | ||
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```bib | ||
@inproceedings{SelectiveDualarmGrasping:IROS2018, | ||
author={Shingo Kitagawa and Kentaro Wada and Shun Hasegawa and Kei Okada and Masayuki Inaba}, | ||
booktitle={Proceedings of The 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems}, | ||
title={Multi-stage Learning of Selective Dual-arm Grasping Based on Obtaining and Pruning Grasping Points Through the Robot Experience in the Real World}, | ||
year={2018}, | ||
month={october}, | ||
pages={7123-7130}, | ||
} | ||
``` |
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# Training and evaluation for models | ||
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## `grasp_fcn`: FCN with grasping point detection | ||
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Please read [experiments/grasp_fcn/README.md](./grasp_fcn/README.md) | ||
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## `dualarm_grasp_fcn`: FCN with dual-arm grasping point detection | ||
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Please read [experiments/dualarm_grasp_fcn/README.md](./dualarm_grasp_fcn/README.md) | ||
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## `occluded_mask_rcnn`: Mask-RCNN with occlusion recognition | ||
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Please read [experiments/occluded_mask_rcnn/README.md](./occluded_mask_rcnn/README.md) | ||
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## `occluded_grasp_mask_rcnn`: Mask-RCNN with occlusion recognition and dual-arm grasping point detection | ||
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Please read [experiments/occluded_grasp_mask_rcnn/README.md](./occluded_grasp_mask_rcnn/README.md) |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/.gitignore
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logs |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/README.md
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# `dualarm_grasp_fcn`: FCN with dual-arm grasping point detection | ||
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## Training | ||
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```bash | ||
python train_dualarm_grasp_fcn32s.py --gpu 0 --config ./cfg/config.yaml | ||
``` |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709061826_config.yaml
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model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 100000 | ||
weight_decay: 0.0005 | ||
alpha: | ||
single: 1.0 | ||
dual: 1.0 | ||
random_state: 1234 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709081604_config.yaml
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model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 100000 | ||
weight_decay: 0.0005 | ||
alpha: | ||
single: 10.0 | ||
dual: 10.0 | ||
random_state: 1234 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709082320_config.yaml
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model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 200000 | ||
weight_decay: 0.0005 | ||
alpha: | ||
single: 100.0 | ||
dual: 100.0 | ||
random_state: 1234 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709091542_config.yaml
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model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 100000 | ||
weight_decay: 0.0005 | ||
alpha: | ||
single: 500.0 | ||
dual: 500.0 | ||
random_state: 1234 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709102200_config.yaml
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model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 200000 | ||
modelpath: out/20170909_154314/models/DualarmGraspFCN32s_iter00148000.npz | ||
weight_decay: 0.0005 | ||
alpha: | ||
single: 100.0 | ||
dual: 100.0 | ||
random_state: 1234 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709102205_config.yaml
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model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 200000 | ||
weight_decay: 0.0005 | ||
alpha: | ||
single: 300.0 | ||
dual: 300.0 | ||
random_state: 1234 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709120008_config.yaml
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alpha: | ||
single: 1.0 | ||
dual: 1.0 | ||
frequency_balancing: True | ||
model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 200000 | ||
random_state: 1234 | ||
weight_decay: 0.0005 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201709120220_config.yaml
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alpha: | ||
single: 10.0 | ||
dual: 10.0 | ||
frequency_balancing: True | ||
model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 200000 | ||
random_state: 1234 | ||
weight_decay: 0.0005 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201801242025_config.yaml
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alpha: | ||
single: 1.0 | ||
dual: 1.0 | ||
dataset_class: 'DualarmGraspDatasetV3' | ||
frequency_balancing: True | ||
model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 200000 | ||
random_state: 1234 | ||
weight_decay: 0.0005 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201801291810_config.yaml
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alpha: | ||
single: 1.0 | ||
dual: 1.0 | ||
dataset_class: 'DualarmGraspDatasetV4' | ||
frequency_balancing: True | ||
model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 200000 | ||
random_state: 1234 | ||
weight_decay: 0.0005 |
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demos/grasp_data_generator/experiments/dualarm_grasp_fcn/cfg/201802161551_config.yaml
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alpha: | ||
single: 1.0 | ||
dual: 1.0 | ||
dataset_class: 'DualarmGraspDatasetV5' | ||
frequency_balancing: True | ||
model: 'dualarm_grasp_fcn32s' | ||
lr: 1.0e-5 | ||
max_iter: 100000 | ||
random_state: 1234 | ||
weight_decay: 0.0005 |
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