Fabio Galasso

Sapienza University of Rome, OSRAM (Germany)

Papers

Total Citations

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About

Fabio Galasso is a leading researcher at the forefront of human-robot interaction and computer vision, with a focused expertise in pose forecasting for industrial collaboration. His most impactful work, "Pose Forecasting in Industrial Human-Robot Collaboration" (2022), has garnered 41 citations and introduces the groundbreaking Separable-Sparse Graph Convolutional Network (SeS-GCN). This innovation marks a significant leap forward by, for the first time, bottlenecking the interaction of spatial, temporal, and channel-wise dimensions in Graph Convolutional Networks, enabling more accurate and efficient prediction of human poses in dynamic industrial settings. Galasso’s contributions directly address the challenge of safe and seamless human-robot teamwork, pushing the boundaries of collaborative robotics. His earlier work, such as "Don’t Turn Off the Lights": Modelling of Human Light Interaction in Indoor Environments (2017), demonstrates a broader interest in understanding human behavior within complex environments. Through his research, Galasso is shaping the future of intelligent automation, making industrial human-robot collaboration safer, more intuitive, and highly efficient—a critical achievement for next-generation manufacturing and robotics.