Lukas Neuner
Papers
1
Total Citations
17
H-Index
1
About
Lukas Neuner’s research lies at the intersection of rehabilitation robotics, human-robot interaction, and machine learning, with a focus on creating adaptive systems that respond to human feedback. His most-cited work, “Using Human Ratings for Feedback Control: A Supervised Learning Approach With Application to Rehabilitation Robotics” (2020, 17 citations), introduces a novel method for personalizing parametric controllers by training a reward model directly from human ratings. This approach, grounded in supervised learning, enables robots to learn optimal behaviors from user input—a breakthrough for gait rehabilitation. Neuner applied this framework to a rehabilitation robot, teaching it to walk patients in a more physiologic, patient-responsive manner. His work bridges the gap between control theory and human-centered design, offering a scalable solution for tailoring robotic assistance to individual needs. With 17 citations, this paper has already influenced research in adaptive robotics and assistive technology. Neuner’s contributions are particularly notable for their practical impact: by enabling robots to learn from subjective human feedback, he opens new pathways for intuitive, safe, and effective human-robot collaboration in clinical settings.
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Top Papers
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