Peter Tisnikar
Papers
1
Total Citations
4
H-Index
1
About
Peter Tisnikar is a researcher at the intersection of human-robot interaction (HRI) and machine learning, with a focus on enabling robots to perceive and respond to social cues in real time. His key research areas include time series classification, affective computing, and the detection of interaction ruptures—moments when human-robot communication breaks down. In his most cited work (2024, 4 citations), Tisnikar developed a novel classification pipeline that uses anonymized speech, posture, and facial features to detect such ruptures, allowing robots to adapt their behavior without compromising user privacy. This contribution is foundational for building more resilient and socially aware autonomous systems. By prioritizing non-invasive sensing and real-time analysis, Tisnikar’s research directly addresses a critical gap in HRI: the need for robots to recognize errors or misunderstandings as they happen. His work has been recognized for its practical methodology and potential to improve user trust and interaction quality in assistive and collaborative robotics.
Research Focus
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Top Papers
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