Max Robotics — Global Service Robot Directory + US Certification

About

Marc Ebner is a researcher whose work lies at the intersection of evolutionary robotics, computer vision, and autonomous mobile systems. His early contributions focus on evolving behavior-based control architectures for mobile robots, demonstrating how genetic programming can automatically generate robust control programs that transfer from simulation to the real world—a foundational challenge in robotics. His 1998 paper on evolving a control architecture (21 citations) and his 1999 work bridging simulation to reality (8 citations) are key references in this area. Ebner also advanced monocular foveated vision for centering behavior (13 citations, 2000) and developed methods for environment model evolution to aid robot localization (6 citations, 1999). More recently, he has tackled the practical problem of collision detection, using logistic regression and IMU data to localize impact points, with papers from 2019 and 2022 (6 and 4 citations, respectively). His work on acceleration-based collision detection (3 citations, 2019) and moving object extraction (2 citations, 1998) rounds out a career dedicated to making autonomous robots more adaptive, safe, and perceptive. Ebner’s research is valuable for students and engineers interested in evolutionary computation, sensor-based robotics, and real-world deployment of intelligent systems.