Train robotics model with integrated curriculum learning-based gripper environment. Choose from different perception layers depth, RGB-D. Run pretrained models with SAC, BDQ and DQN algorithms. Test trained algorithms in different scenes and domains.
Master's thesis PDF
Install anaconda. Start a clean conda environment.
conda create -n grasp_env python=3.6
conda activate grasp_env
python manipulation_main/training/train_stable_baselines.py train --config config/gripper_grasp.yaml --algo SAC --model_dir trained_models/SAC_full --timestep 100000 -v
conda create -n grasp_env python=3.6
conda activate grasp_env
conda install -c conda-forge cudatoolkit=10.0 cudnn=7.6.5
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
In setup.py change:
'tensorflow==1.14.0',
to
'tensorflow_gpu==1.14.0',
Use pip to install the dependencies.
pip install -e .
If using GPU you can check if it was successful with: ´´´ python -c "import tensorflow as tf; print(tf.config.experimental.list_physical_devices('GPU'))" ´´´
train_stable_baselines script provides the functionality of running and training models.
For running models 'manipulation_main/training/train_stable_baselines.py' takes the following arguments
- --model - trained model file e.g trained_models/SAC_full_depth_1mbuffer/best_model/best_model.zip
- -t - use test dataset if not given runs on training dataset
- -v - visualize the model (faster without the -v option)
- -s - run stochastic model if not deterministic
For running functionality run sub-parser needs to be passed to the script.
python manipulation_main/training/train_stable_baselines.py run --model trained_models/SAC_full_depth_1mbuffer/best_model/best_model.zip -v -t
For training models 'manipulation_main/training/train_stable_baselines.py' takes the following arguments
- --config - config file (e.g 'config/simplified_object_picking.yaml' or 'config/gripper_grasp.yaml')
- --algo - algorithm to use(e.g BDQ, DQN, SAC, TRPO)
- --model_dir - name of the folder to host the trained model logs and best performing model on validation set.
- -sh - use shaped reward function (Only makes sense for Full Environment version)
- -v - visualize the model
For training functionality train sub-parser needs to be passed to the script.
python manipulation_main/training/train_stable_baselines.py train --config config/gripper_grasp.yaml --algo SAC --model_dir trained_models/SAC_full --timestep 100000 -v
To run the gripperEnv related test use
pytest tests_gripper
- Domain and Scene Transfer
- Different Perception Layers
- Baris Yazici - Initial work - BarisYazici
To cite the master's thesis:
@MastersThesis{Yazici2020,
author = {Yazici Baris},
title = {{Branch Dueling Deep Q-Networks for Robotics Applications}},
school = {Technical University of Munich},
year = {2020},
howpublished = {\url{https://github.com/BarisYazici/tum_masters_thesis}}
}
This project is licensed under the MIT License - see the LICENSE.md file for details
- Mahmoud Akl (supervisor)
- Breyer Michel (author of https://arxiv.org/abs/1803.04996)
- https://github.com/atavakol/action-branching-agents