Yue Leire Erro Nuin
Papers
2
Total Citations
33
H-Index
2
About
Yue Leire Erro Nuin is a robotics researcher whose work bridges the gap between simulation and real-world reinforcement learning. Her primary research areas include robot learning, deep reinforcement learning (DRL), and modular robotics, with a focus on integrating these techniques with standard robotic middleware. Her most impactful contribution is the development of **gym-gazebo2**, a toolkit that upgrades the original gym-gazebo to work with ROS 2 and Gazebo, enabling more realistic, real-world-oriented RL training. This work, cited 25 times, provides a crucial software architecture for researchers to train robots in simulation before deployment. Additionally, she proposed **ROS2Learn**, a framework for DRL in modular robotics that allows robots to learn directly from joint states using state-of-the-art algorithms like PPO and TRPO. With a total of 33 citations across her top works, Erro Nuin’s contributions are foundational for students and researchers seeking to apply RL in robotics, offering accessible, open-source tools that streamline the transition from simulation to physical hardware.
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