Stefan Zeiss

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About

Stefan Zeiss is a leading researcher in robotic assembly, whose work focuses on enabling robots to perform complex, contact-rich tasks with greater autonomy and reliability. His key research areas include force estimation, skill modeling, and the development of reusable robotic primitives. Zeiss made a major contribution to the field with his highly cited 2014 paper on contact force estimation for robotic assembly using motor torques, which demonstrated how to detect environmental contacts without dedicated force-torque sensors—a cost-effective and technically flexible solution that has garnered 33 citations. Building on this, his 2015 work introduced a novel framework for modeling Robotic Assembly Skills (RAS), combining pose-wrench and state machine representations to create generic, reusable skill templates. This approach allows elementary robot tasks to be coordinated, controlled, and supervised across different applications, earning 15 citations. Zeiss’s research is notable for its practical impact on industrial automation, offering pathways to reduce sensor costs while improving robotic adaptability. His work is essential reading for students and engineers seeking to advance autonomous assembly systems.