Alejandro Solano Rueda

About

Alejandro Solano Rueda is a robotics researcher whose work sits at the intersection of reinforcement learning and robot operating systems, with a particular focus on developing practical frameworks that bridge the gap between simulation and real-world robotic applications. He is best known for his contributions to open-source robotics toolkits, most notably his work on **gym-gazebo2**, an upgraded reinforcement learning toolkit built on ROS 2 and Gazebo that complies with the widely adopted OpenAI Gym interface. This work, which has garnered 25 citations since its 2019 publication, addresses the critical challenge of making reinforcement learning environments more applicable to real-world robotic deployments. Complementing this, Solano Rueda co-developed **ROS2Learn**, a deep reinforcement learning framework for modular robotics that enables training directly from joint states using state-of-the-art algorithms including Proximal Policy Optimization and Trust Region Policy Optimization, accumulating 8 citations. Together, these contributions have helped establish a more standardized and accessible pipeline for robotics researchers exploring deep reinforcement learning, making advanced training methodologies more accessible to the broader ROS community. His work represents an important step toward closing the simulation-to-reality gap in autonomous robotics research.

Research Focus

Key Achievements

Top Papers

1. 1
   gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo

   25 citations · 2019
2. 2
   ROS2Learn: a reinforcement learning framework for ROS 2

   8 citations · 2019

Key Collaborators

Contact & Links