Sonja Husmann

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About

Sonja Husmann is a robotics researcher whose work bridges the critical gap between industrial automation and human-centered rehabilitation. Her primary research areas include model predictive control for robotic manufacturing, iterative learning control for rehabilitation robotics, and intelligent assist-as-needed strategies for human-robot interaction. Husmann’s most impactful contribution is her 2019 paper on model predictive force control in grinding using lightweight robots, which has garnered 11 citations and addresses the pressing issue of labor shortages in the mould and die sector by automating strenuous manual tasks. In rehabilitation, she has pioneered adaptive control methods that allow robots to provide only the necessary level of support during therapy, promoting patient engagement and neuroplasticity. Her 2020 work on iterative learning control for gravity compensation in upper-arm rehabilitation (6 citations) and her 2019 fuzzy logic control approach (5 citations) demonstrate her commitment to developing intelligent, responsive robotic systems that can work safely alongside humans. Through these contributions, Husmann is advancing both industrial automation and assistive technology, making manufacturing more competitive while improving the quality of life for patients recovering from stroke and other motor impairments.