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README.md
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### [ACT](./robo_manip_baselines/act)
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Action Chunking with Transformers
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### [DiffusionPolicy](./robo_manip_baselines/diffusion_policy)
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Diffusion Policy
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### [MT-ACT](./robo_manip_baselines/mt_act)
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Multi-Task Action Chunking Transformer
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## Data
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### Publicly available datasets
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### Data collection by teleoperation
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## Environments for robot manipulation
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## Utilities
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## Evaluation results
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## License
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Files that originate from this repository are subject to the BSD 2-Clause License. If a file explicitly states a different license, or if there are different license files in a directory, those licenses will take precedence. For files in third-party directories, please follow the respective licenses.
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## Citation
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You can cite this work with:
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```bib
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@
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author = {
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title = {
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}
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```
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# Spatial attention recurrent neural network (SARNN)
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## Install
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See [GitHub](https://github.com/isri-aist/RoboManipBaselines/tree/master/robo_manip_baselines/sarnn) for installation.
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## Policy rollout
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Run a trained policy:
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```console
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$ python ./bin/rollout/RolloutSarnnMujocoUR5eCable.py \
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--checkpoint <checkpoint_path>/SARNN.pth \
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--cropped_img_size 280 --skip 6 --world_idx 0
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```
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The `--cropped_img_size` option must be the same as for dataset generation.
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## Technical Details
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For more information on the technical details, please see the following paper:
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```bib
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@INPROCEEDINGS{SARNN_ICRA2022,
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author = {Ichiwara, Hideyuki and Ito, Hiroshi and Yamamoto, Kenjiro and Mori, Hiroki and Ogata, Tetsuya},
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title = {Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility},
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booktitle = {International Conference on Robotics and Automation},
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year = {2022},
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pages = {5375-5381},
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doi = {10.1109/ICRA46639.2022.9811940}
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}
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```
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