Kasper Hald
Papers
9
Total Citations
89
H-Index
5
About
Kasper Hald is a robotics and human-robot interaction researcher whose work centers on trust dynamics in close-proximity industrial human-robot collaboration. His most significant contributions lie in developing novel, non-intrusive methods for assessing human trust in robotic systems in real time — a challenge that traditional post-interaction questionnaires fail to adequately address. Hald's most cited work (31 citations) investigates how varying levels of mistake explanation from a virtual robot can repair human trust following errors, offering practical insights for designing more transparent robotic systems. Alongside this, his research pioneered the use of motion tracking and galvanic skin response as physiological and behavioral proxies for trust, with early studies (16 citations each) establishing foundational frameworks for detecting physical apprehension signals during human-robot tasks. His later work advances these methods through deep learning-based automatic trust estimation and IMU-based motion tracking, while a benchmark dataset published in 2024 provides the research community with a valuable resource for data-driven trust assessment. Collectively accumulating nearly 90 citations, Hald's research bridges robotics, psychology, and human factors engineering, making meaningful strides toward safer, more adaptive, and trustworthy collaborative robot systems in industrial environments.
Research Focus
Key Achievements
Top Papers
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- 5Testing Augmented Reality Systems for Spotting Sub-Surface Impurities6 citations · 2018
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