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About

Lander Usategui San Juan is a robotics researcher whose work sits at the intersection of real-time systems, robot middleware, reinforcement learning, and cybersecurity. His most influential contributions center on evaluating and advancing the Robot Operating System 2 (ROS 2) as a foundation for reliable, distributed robotic applications. His 2018 benchmarking study of ROS 2.0 communications for real-time robotics has garnered 40 citations, establishing him as a key voice in assessing the practical viability of next-generation robot middleware. Complementing this, his analysis of the Linux communication stack for real-time robotics and his advocacy for Time-Sensitive Networking (TSN) as a future standard for robotic communications reflect a deep commitment to deterministic, dependable robot control architectures. Beyond communications infrastructure, Usategui San Juan has made notable contributions to reinforcement learning tooling for robotics, co-developing gym-gazebo2 and ROS2Learn — frameworks that lower the barrier for applying deep reinforcement learning in realistic simulated environments. Perhaps most distinctively, he has helped pioneer the emerging field of robot cybersecurity, introducing the Robot Vulnerability Database (RVD) and the Robotics CTF platform to bridge the historically wide gap between robotics and security research. Collectively, his work provides foundational infrastructure that benefits both academic researchers and practitioners building the next generation of intelligent, secure robotic systems.