https://github.com/berkeleyautomation/orienting_novel_3d_objects
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms
https://github.com/berkeleyautomation/orienting_novel_3d_objects
Last synced: about 1 month ago
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Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms
- Host: GitHub
- URL: https://github.com/berkeleyautomation/orienting_novel_3d_objects
- Owner: BerkeleyAutomation
- Created: 2019-04-01T21:15:01.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-15T21:18:29.000Z (over 2 years ago)
- Last Synced: 2025-03-26T20:49:27.998Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 119 MB
- Stars: 2
- Watchers: 5
- Forks: 3
- Open Issues: 13
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Metadata Files:
- Readme: README.md
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README
**Installation**:
Create a python3 virtual environment and after enabling the data generation virtual environment, install the necessary requirements as follows:
1. Run pip install requirements.txt to install all external requirements
2. Run python setup.py develop to install unsupervised_rbt package
**Basic Usage**:
You will mostly interact with files in the tools folder, which all have corresponding files in the cfg/tools/ folder
which specify paramaters for these files.
The controller folder contains scripts for running the simulation experiments to orient objects given a model trained on the self-supervised trask.
**Self-Supervised Rotation Prediction Task**:
Data Generation: See tools/data_gen_quat.py for generating data for the task. See cfg/tools/data_gen_quat.yaml for config parameters
The dataset used for Kit-Net is called 872objv3
Example usage: python tools/data_gen_quat.py {dataset_name}
Training: Make sure to either generate data or use pre-generated data. For training see tools/unsup_rbt_train_quat.py
Example usage: python tools/unsup_rbt_train_quat.py {dataset_name}. Example dataset is 872objv3
Testing: Same as train except with a --test flag.
Example usage: python tools/unsup_rbt_train_quat.py {dataset_name} --test. Example dataset is 872objv3
**Prismatic Cavity Task**:
Create a dataset with tools/prismatic_cavity.py, then do training and testing as above
**Simulation Experiments Controller**:
In controller/pyrender_controller.py you can run the orienting objects experiments.
In controller/prism_controller.py you can run the simulation experiments for Kit-Net