robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots
This toolkit reduces the gap between simulation and real-world robotics, making DRL more accessible for researchers and practitioners working with industrial robots.
The authors tackled the challenge of applying Deep Reinforcement Learning (DRL) to robotics by developing robo-gym, an open-source toolkit that enables seamless transfer from simulation to real-world robots, demonstrated with applications on a mobile robot and a robot arm.
Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional work and fine-tuning to make the setup work effectively. In order to increase the use of DRL with real robots and reduce the gap between simulation and real world robotics, we propose an open source toolkit: robo-gym. We demonstrate a unified setup for simulation and real environments which enables a seamless transfer from training in simulation to application on the robot. We showcase the capabilities and the effectiveness of the framework with two real world applications featuring industrial robots: a mobile robot and a robot arm. The distributed capabilities of the framework enable several advantages like using distributed algorithms, separating the workload of simulation and training on different physical machines as well as enabling the future opportunity to train in simulation and real world at the same time. Finally we offer an overview and comparison of robo-gym with other frequently used state-of-the-art DRL frameworks.