Elias Barba Moral
Papers
2
Total Citations
33
H-Index
2
About
Elias Barba Moral is a robotics researcher specializing in the intersection of reinforcement learning and robotic systems, with a particular focus on developing open-source frameworks that bridge the gap between simulation environments and real-world robotic applications. His work centers on leveraging the Robot Operating System (ROS) and Gazebo simulation platform to create accessible, standardized tools for training intelligent robotic agents. Barba Moral's most recognized contribution is **gym-gazebo2** (2019), an upgraded reinforcement learning toolkit built on ROS 2 and compatible with the widely adopted OpenAI Gym interface. With 25 citations, this work reflects growing community interest in simulation-based robot learning pipelines designed with real-world deployment in mind. Complementing this, his **ROS2Learn** framework (2019) introduces a deep reinforcement learning environment for modular robotics, enabling robots to learn directly from joint states using state-of-the-art algorithms including Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), and Actor-Critic methods. Together, these contributions position Barba Moral as a meaningful contributor to the emerging field of robot learning infrastructure, helping lower the barrier for researchers and engineers seeking to apply modern deep reinforcement learning techniques within standardized robotic development ecosystems.
Research Focus
Key Achievements
Top Papers
- 1gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo25 citations · 2019
- 2ROS2Learn: a reinforcement learning framework for ROS 28 citations · 2019