Papers
58
Total Citations
836
H-Index
14
About
Sabina Jeschke is a pioneering researcher at the intersection of robotics, artificial intelligence, and engineering education, whose work has significantly shaped our understanding of human-robot collaboration in the context of Industry 4.0. Her research spans automated emotion recognition, reinforcement learning for industrial robotics, and the design of hybrid human-robot workplaces — areas that collectively address how humans and intelligent machines can work together effectively and safely. Among her most influential contributions is her work on EEG-based emotion recognition (134 citations), which bridges affective computing and human-robot interaction, offering practical tools for industry applications. Equally impactful is her sustained investigation into hybrid teams — workplaces where robots function as genuine collaborators rather than mere tools — exploring dimensions from anthropomorphism and subjective stress to workplace design and robotic motion planning. Her reinforcement learning approach to industrial robot motion planning (78 citations) highlights her commitment to adaptive, real-world solutions. Jeschke has also been a vocal advocate for reimagining engineering education, demonstrating through empirical research how virtual worlds and human-robot collaboration can prepare future engineers for digitally transformed industries. With hundreds of citations across her body of work, her influence extends from factory floors to lecture halls, making her a defining voice in the future of intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
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- 3Motion Planning for Industrial Robots using Reinforcement Learning78 citations · 2017
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- 7Design of a Robotic Workmate27 citations · 2017
- 8Hybrid teams of industry 4.0: A work place considering robots as key players20 citations · 2017
- 9Subjective Stress in Hybrid Collaboration20 citations · 2017
- 10Robotic Workmates : Hybrid Human-Robot-Teams in the Industry 4.018 citations · 2016