Iker Zamora
Papers
1
Total Citations
90
H-Index
1
About
Iker Zamora is a robotics researcher whose work lies at the intersection of reinforcement learning and robotic simulation. His most impactful contribution, the paper "Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo" (2016, 90 citations), provides a foundational software architecture that bridges the gap between popular machine learning frameworks and realistic robotic environments. By integrating the OpenAI Gym interface with the Robot Operating System (ROS) and the Gazebo simulator, Zamora enabled researchers to train reinforcement learning agents—specifically using Q-Learning and Sarsa—in high-fidelity, physics-based simulations. This toolkit has become a critical resource for the robotics and AI communities, allowing for safer, more efficient development and testing of autonomous behaviors before deployment on physical hardware. His work is notable for democratizing access to complex robotic simulation, making it easier for students and researchers to experiment with reinforcement learning in realistic settings. With 90 citations, this paper remains a key reference for anyone looking to apply RL to real-world robotic tasks.
Research Focus
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Top Papers
- 1