Papers
8
Total Citations
169
H-Index
6
About
Miguel Prada is a leading researcher in human-robot interaction and collaborative robotics, with a core focus on developing intuitive, physically interactive robotic systems. His most significant contributions center on the application of Dynamic Movement Primitives (DMPs) for object handover tasks, where he has pioneered control systems that enable robots to fluidly and safely exchange objects with human partners. His highly cited 2013 and 2014 papers (garnering 47 and 44 citations respectively) established foundational models for trajectory generation and experimental validation in this domain, demonstrating how robots can adapt to unknown handover locations in real-time. Prada’s work uniquely bridges computational modeling with human behavioral observation, as evidenced by his studies on the relative importance of spatial versus temporal precision for user satisfaction (26 citations). Beyond handovers, his research extends to teleoperation stability with variable time-delays and dual-arm co-manipulation of flexible objects for industrial applications. His recent work (2024) explores robot learning from teleoperated demonstrations in unstructured construction environments, showcasing his commitment to deploying collaborative robots in real-world, dynamic settings. With a career spanning foundational theory to applied validation, Prada’s research is essential reading for anyone interested in making physical human-robot collaboration safe, fluent, and socially acceptable.
Research Focus
Key Achievements
Top Papers
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- 5Asymptotic stability of teleoperators with variable time-delays15 citations · 2009
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