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About

Raffaello Camoriano is a leading researcher in robot learning and humanoid robotics, whose work bridges imitation learning, whole-body control, and human-machine interfaces. His core contributions span dynamic movement primitives (DMPs) for feasible skill acquisition on humanoid platforms, structured prediction approaches for imitation learning, and long-term myoelectric control for upper-limb prostheses using high-density sEMG and incremental learning. Notably, his 2018 paper on constrained DMPs (21 citations) established a framework for combining movement primitives with reinforcement learning to ensure physically feasible motions on humanoid robots, while his 2022 ADHERENT work (17 citations) advanced human-like trajectory generation for whole-body control. Camoriano has also innovated in specialized robotic manipulation, including a novel gripper for garment handling (13 citations) within the CloPeMa project, and in multi-path learning for industrial spray painting via PaintNet (7 citations). His research demonstrates remarkable breadth—from foundational inverse dynamics modeling (4 citations) to addressing domain shift in object detection for robotics (3 citations). With over 100 total citations across his most-cited works, Camoriano’s contributions are shaping how robots learn complex motor skills, interact with deformable objects, and adapt to real-world variability, making him a pivotal figure in modern robotics research.