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About

Bernhard Jung is a robotics researcher whose work sits at the intersection of physical human-robot interaction, machine learning, and autonomous systems. His research has fundamentally advanced how robots perceive, adapt to, and learn from human partners, with particular emphasis on enabling robots to respond intelligently to physical contact and collaboration. Jung's most influential contributions center on computational models for mutual adaptation in human-robot interaction, with his 2012 paper on this topic accumulating 132 citations. A recurring theme across his work is the challenge of detecting and compensating for external perturbations during cooperative tasks—an area where he pioneered the application of Dynamic Mode Decomposition as a sensor-free machine learning solution, generating over 150 additional citations across related publications. His imitation learning framework, which enables robots to acquire interaction skills from observing human-human demonstrations, represents a significant methodological advance in data-driven robotics. Beyond close-proximity collaboration, Jung has extended his expertise to autonomous robot design for hazardous underground mining environments and to precision tracking evaluation in extended reality systems. His earlier work on kinesthetic bootstrapping demonstrated a natural, hands-on approach to teaching humanoid robots motor skills. Collectively accumulating over 540 citations, Jung's research portfolio reflects a sustained and impactful commitment to making robots genuinely responsive partners in complex, real-world human environments.