Sebastian Stabinger

Papers

Total Citations

H-Index

About

Sebastian Stabinger’s research lies at the intersection of human-robot interaction and intelligent software testing for autonomous systems. His most cited work, “The Effects of Social Gaze in Human-Robot Collaborative Assembly” (32 citations), investigates how non-verbal cues—specifically, a robot’s gaze—can enhance trust and efficiency in shared tasks, offering foundational insights for designing more intuitive collaborative robots. In a contrasting but equally forward-looking contribution, Stabinger explores “Autonomous skill-centric testing using deep learning,” addressing the critical challenge of verifying robotic software in unpredictable, real-world environments. By proposing a deep learning-driven approach to generate and execute test cases, he tackles the high cost and time constraints of traditional simulation-based testing. Though early in his career, Stabinger’s work bridges social robotics and robust software engineering, demonstrating a dual commitment to making robots both more human-aware and more reliable. His research is particularly relevant for students and engineers developing autonomous systems that must operate safely alongside people.