Max Robotics — Global Service Robot Directory + US Certification

About

Paul Szenher is a robotics researcher whose work spans autonomous navigation, perception, and manipulation in challenging, real-world environments. His primary research areas include simultaneous localization and mapping (SLAM), reinforcement learning for exploration and planning, and sensor enhancement for mobile robots. Szenher’s most impactful contribution is his work on simulation-based lidar super-resolution for ground vehicles, which has garnered 83 citations and demonstrates a practical method for enhancing sparse 3D lidar data using deep learning and driving simulators. He also developed DRACo-SLAM, a distributed SLAM system for underwater robot teams using imaging sonar, addressing the critical challenge of multi-robot coordination in GPS-denied environments. His research on zero-shot reinforcement learning for autonomous exploration under uncertainty, with 18 citations, showcases a novel framework for transferring exploration policies learned in simulation to new environments. More recently, Szenher has applied his expertise to robust route planning using distributional reinforcement learning and autonomous mobile manipulation for substation inspection, highlighting his commitment to deploying reliable robotic systems in high-stakes, real-world settings.