Alessio Sampieri

Papers

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About

Alessio Sampieri is a researcher at the forefront of industrial human-robot collaboration, with a primary focus on pose forecasting and graph-based deep learning. His most impactful work, "Pose Forecasting in Industrial Human-Robot Collaboration" (2022, 41 citations), introduces the Separable-Sparse Graph Convolutional Network (SeS-GCN)—a novel architecture that, for the first time, bottlenecks the interaction of spatial, temporal, and channel-wise dimensions in Graph Convolutional Networks. This innovation enables more efficient and accurate prediction of human poses in dynamic industrial settings, directly enhancing the safety and fluidity of human-robot teamwork. Sampieri’s contributions address a critical challenge in collaborative robotics: anticipating human motion to prevent collisions and optimize task coordination. By pushing back the frontiers of industrial automation, his work has garnered significant attention from both academia and industry, laying the groundwork for smarter, more responsive robotic systems. His research exemplifies how advanced graph neural networks can bridge the gap between human intent and robotic action, making him a key figure in the evolution of Industry 5.0.