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About

Elisa Maiettini is a robotics and computer vision researcher whose work sits at the dynamic intersection of deep learning, human-robot interaction, and robotic manipulation. Her research addresses one of the central challenges in modern robotics: enabling robots to perceive, learn from, and interact with the world in ways that are both efficient and adaptable to real-world conditions. Maiettini has made significant contributions to object detection for humanoid robots, pioneering interactive and weakly supervised data collection strategies that dramatically reduce the labeling burden typically associated with deep learning systems. Her most cited work (28 citations) introduced an interactive framework for training object detectors on humanoid platforms, while subsequent studies explored fast, online-learning approaches suited to the dynamic demands of robotic environments. She has also advanced the field of hand-object interaction, studying how human manipulation strategies can inspire robotic grasping, and has explored multi-fingered grasping through deep reinforcement learning combining vision and touch. More recently, Maiettini has turned her attention to social robotics, developing learning-based systems for mutual gaze estimation and human attention prediction that allow robots like the iCub to engage meaningfully in joint human-robot tasks. With over 130 cumulative citations across a focused body of work, her research represents a compelling effort to make robots genuinely perceptive and socially aware partners.