Train deep reinforcement learning model for robotics grasping. Choose from different perception layers raw Depth, RGBD and autoencoder. Test the learned models in different scenes and object datasets
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
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
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
Ablation Studies
Training Environment
Domain transfer performance
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