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

Prerequisites

Install anaconda. Start a clean conda environment.

conda create -n grasp_env python=3.6
conda activate grasp_env

Installation

Use pip to install the dependencies. If you have a gpu you might need to install tensorflow based on your system requirements.

pip install -e .

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