deep-rl-grasping

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

tum_masters_thesis

Master's thesis in Computer Science at Technical University of Munich. Written in latex.

bdq_sb

Fork of stable baselines, provides implementation of BDQ algorithm https://arxiv.org/abs/1711.08946

umi_robot_ros2

ROS 2 hardware interface for UMI robot.