Alessia Vignolo
Italian Institute of Technology, University of Warwick, University of Genoa
Papers
14
Total Citations
153
H-Index
6
About
Alessia Vignolo is a leading researcher at the intersection of cognitive robotics, human-robot interaction, and action perception. Her work focuses on enabling robots to understand and respond to human motion intuitively, drawing inspiration from the biological mechanisms that underpin human social cognition. Vignolo’s major contributions include developing computational models for detecting biological motion—a foundational skill for safe human-robot collaboration—and demonstrating how these models can enhance the attentive capabilities of humanoid robots like iCub. Her research on human motion understanding has been pivotal in selecting optimal action timing for collaborative tasks, with her most cited work, "Detecting Biological Motion for Human–Robot Interaction," accumulating 38 citations. She is also the creator of the MoCA dataset (22 citations), a bi-modal resource of kinematic and multi-view video data for fine-grained cooking actions, which has become a key benchmark for studying view-invariant action properties. Notably, Vignolo’s studies on adaptive robot teachers show that effortful robot behavior boosts human partners’ learning and commitment, with implications for education and rehabilitation. Her work has been published in top venues like *Frontiers in Robotics and AI* and *Scientific Reports*, establishing her as a pioneer in making robots more perceptive and socially aware partners.
Research Focus
Key Achievements
Top Papers
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- 7Action similarity judgment based on kinematic primitives6 citations · 2020
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- 9The complexity of biological motion5 citations · 2016
- 10Learning dictionaries of kinematic primitives for action classification5 citations · 2021