Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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
| Date | Stars |
|---|---|
| 2026-07-31 | 276 |
| 2026-08-01 | 276 |
| 2026-08-02 | 276 |
| 2026-08-06 | 276 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Deep Reinforcement Learning on Robotics Grasping
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](https://github.com/BarisYazici/masters_thesis/blob/master/final_report.pdf)
### Prerequisites (CPU)
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
### Prerequisites (GPU)
```
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](setup.py) change:
```
'tensorflow==1.14.0',
to
'tensorflow_gpu==1.14.0',
```
## Installation
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'))"
´´´
## Run Models
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
```
## Train models
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
```
## Running the tests
To run the gripperEnv related test use
```
pytest tests_gripper
```
* **Domain and Scene Transfer**
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/testtraining.jpg" width="75%">
* **Different Perception Layers**
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/NewPerception.png" width="75%">
* **Ablation Studies**
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/ablation/SAC_performance_shaped_reward_vs_sparse_reward.png" width="45%">
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/ablation/SAC_performance_wo_actuator_width.png" width="45%">
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/ablation/SAC_performance_wo_curriculum_strategy.png" width="45%">
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/ablation/SAC_performance_wo_normalization.png" width="45%">
* **Training Environment**
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/trainingEnv.gif" width="50%">
* **Domain transfer performance**
<img src="https://github.com/BarisYazici/masters_thesis/blob/master/figures/SACGripperEnvRes.png">Excerpt of 5,049 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c17c4e2cfbf9b1cc, llm:Repository description: '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' and topics: 'deep-learning, kuka, machine-learning, open-ai, pybullet, reinfrocement-learning, robotics'.
matched fp:c17c4e2cfbf9b1cc, llm:Repository description: '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' and topics: 'deep-learning, kuka, machine-learning, open-ai, pybullet, reinfrocement-learning, robotics'.
matched fp:c17c4e2cfbf9b1cc, llm:Repository description: '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' and topics: 'deep-learning, kuka, machine-learning, open-ai, pybullet, reinfrocement-learning, robotics'.
matched fp:c17c4e2cfbf9b1cc, llm:Repository description: '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' and topics: 'deep-learning, kuka, machine-learning, open-ai, pybullet, reinfrocement-learning, robotics'.