Felix Ebert

Papers

Total Citations

H-Index

About

Felix Ebert’s research sits at the compelling intersection of autonomous robotics and human-machine interaction, with a particular focus on enabling intelligent systems to operate seamlessly in complex, real-world environments. His major contributions span two key areas: adaptive brain-computer interfaces (BCIs) and robust long-range autonomous navigation. In his highly cited 2017 work on goal-recognition-based BCIs, Ebert pioneered self-adaptive strategies to overcome the fundamental bandwidth bottleneck between low-rate EEG signals and demanding robotic control tasks—a principled approach that allows users to fluently navigate immersive robotic systems through thought alone. His equally influential 2017 paper on teach-and-repeat navigation presents a robust system for autonomous cars operating in non-urban settings, where a vehicle learns a route by following a human guide tracked via marker-less LiDAR, then reliably repeats the path for transportation tasks. While his citation counts (10 and 6 respectively) reflect a focused, early-career impact, Ebert’s work is notable for its practical, systems-level thinking—tackling real bottlenecks in field robotics and assistive technology. His contributions offer a blueprint for making autonomous systems more adaptive, intuitive, and deployable beyond structured environments.