Papers
18
Total Citations
455
H-Index
9
About
Sven Magg is a robotics and artificial intelligence researcher whose work sits at the intersection of human-robot interaction, machine learning, and cognitive modeling. He is perhaps best known for his pioneering contributions to **interactive reinforcement learning (IRL)**, demonstrating how robots can efficiently acquire skills through real-time human guidance — including speech-based instruction in domestic settings — with his foundational 2016 paper accumulating 91 citations and a complementary 2015 study drawing 56 more. Magg has also played a central role in developing **NICO (Neuro-Inspired COmpanion)**, a humanoid robot platform designed to bridge robotics, neuroscience, and cognitive science for multimodal human-robot interaction, a paper that has garnered 79 citations. His research further extends into deep learning for perception, including a multichannel convolutional neural network for hand posture recognition (77 citations), continual learning of facial expressions, and sentiment estimation from dialogue. More recently, Magg has explored **social robotics and personality modeling**, investigating how context-dependent personality traits can enhance robot-human interaction quality, a paper earning 52 citations. Across his career, his work consistently advances robots that learn, adapt, and interact more naturally alongside humans.
Research Focus
Key Achievements
Top Papers
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- 3A Multichannel Convolutional Neural Network for Hand Posture Recognition77 citations · 2014
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- 6Agent-advising approaches in an interactive reinforcement learning scenario20 citations · 2017
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