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About

Irati Rasines is a robotics researcher focused on enabling robots to learn and adapt in unstructured, real-world environments. Her work spans human-robot interaction, learning from demonstration, and autonomous trajectory generation. Rasines’ most cited paper (2023, 8 citations) introduces a novel algorithm for averaging multivariate time series using Constrained Dynamic Time Warping, addressing a fundamental challenge in robotic motion analysis. She has made significant contributions to feature selection for hand pose recognition in human-robot object exchange (2014, 4 citations), systematically identifying optimal visual features for gesture-based interaction. Her 2024 review (3 citations) synthesizes the fusion of Dynamic Movement Primitives and Artificial Potential Fields, providing a roadmap for adaptive robot behavior. Notably, Rasines is pioneering the application of robot learning from teleoperated demonstrations in construction sites (2025, 2 citations), automating hazardous tasks like mastic deposition. She has also explored robotizing sterility testing in laboratories (2022, 2 citations), demonstrating her versatility in bringing agile robots into controlled environments. With a growing citation record and work spanning 2022-2025, Rasines is establishing herself as a researcher who bridges foundational robotics algorithms with practical, high-impact applications in challenging domains.