Artificial Intelligence researchers at this robotics startup propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics. Code in Github.
ROS2Learn provides an approach which trains a robot directly from joint states, with traditional robotic tools.
“We use a state-of-the-art implementation of the Proximal Policy Optimization, Trust Region Policy Optimization and Actor-Critic Kronecker-Factored Trust Region algorithms to learn policies in four different environments around MARA modular robotic arm.” explains Risto Kojcev, Head of AI.
In a paper made available today here, the team of researchers describe baseline implementations for the most common DRL techniques for policy iteration methods. And using this framework they present the results obtained benchmarking DRL methods in a modular robotic arm with 6 degrees-of-freedom (DoF).
Using a framework that communicates with typical tools used in robotics, such as Gazebo and ROS 2 allows a more realistic representation of the environment. Moreover, they also compare the robustness of the performance of such methods in modular robots with an empirical study in simulation.
The results show that the proposed framework is stable during training of neural networks trough RL with policy-based methods.
- The same AI team has also presented a paper introducing and describing gym-gazebo2, a new toolkit for reinforcement learning using ROS 2 and Gazebo.
- ROS2Learn tutorials
- Instructions for installation
- More on the usage