Michael Paulitsch
Papers
4
Total Citations
56
H-Index
4
About
Michael Paulitsch is a researcher working at the intersection of robotics, autonomous systems, and functional safety — areas that have become increasingly critical as AI-driven automation reshapes industries. His work addresses some of the most pressing challenges in deploying intelligent systems in real-world environments, from factory floors to autonomous vehicles. Paulitsch's most-cited contribution, "Towards Factory-Scale Edge Robotic Systems" (2022, 25 citations), tackles the practical constraints of mobile multi-robot systems, exploring how robots can leverage wireless communication and edge computing to overcome limitations in onboard processing and battery life. His research on fault detection in perception systems (2020, 18 citations) advances trustworthy environment sensing for self-driving vehicles and intelligent robots — a notoriously difficult problem when systematic faults evade traditional diagnostics. Particularly notable is his sustained focus on the safety of deep neural networks under hardware failure conditions. Through multiple works examining convolutional neural networks and object detection systems, Paulitsch investigates how soft errors in hardware can compromise safety-critical AI applications, proposing practical mitigation strategies such as activation range supervision. His cumulative body of work makes him a meaningful voice in the emerging discipline of safety engineering for AI-powered autonomous systems.
Research Focus
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Top Papers
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