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About

Pietro Morerio is a leading researcher at the intersection of robotics, computer vision, and human-robot interaction (HRI). His work focuses on enabling autonomous systems to perceive, understand, and interact with the physical world, with key contributions in 3D shape completion for robotic grasping, self-supervised learning for object detection, and imitation learning for collaborative robots. Morerio’s most cited work, "3DSGrasp: 3D Shape-Completion for Robotic Grasp" (2023, 23 citations), addresses a critical challenge in real-world manipulation: incomplete point cloud data from sparse viewpoints. By leveraging 3D shape completion, his system generates robust grasps even with partial visual input, bridging the gap between perception and action. He also introduced XBG (eXteroceptive Behaviour Generation), a multimodal end-to-end imitation learning system for whole-body humanoid robots in real-world HRI scenarios, enabling natural collaboration through learned behaviors. Morerio’s work is notable for its practical impact, advancing robots from controlled labs to dynamic environments, and his self-training exploration method for object detection further underscores his commitment to scalable, data-efficient learning. With a growing citation record, Morerio is shaping the future of autonomous, interactive robotics.