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About

Diego Ferigo is a robotics researcher whose work sits at the intersection of humanoid robotics, reinforcement learning, and human-robot interaction. His major contributions include the development of Gym-Ignition (24 citations), a framework that creates reproducible robotic simulations for reinforcement learning by interfacing with the next-generation Gazebo simulator. Ferigo has also advanced skill learning on humanoid robots through constrained Dynamic Movement Primitives (DMPs) for feasible skill acquisition (21 citations) and the ADHERENT framework (17 citations), which generates human-like whole-body trajectories. His research on force-myography controlled bionic hands (40 citations) demonstrates practical applications in prosthetics, while his work on minimal intervention control and multi-task sequencing addresses fundamental challenges in imitation learning. Ferigo's multi-sensor approach to biomimetic prosthetic control and whole-body geometric retargeting for humanoid teleoperation further showcase his breadth. With over 130 total citations across his most-cited works, Ferigo has established himself as a researcher who bridges simulation and real-world robotics, creating reproducible frameworks that enable others to advance the field of humanoid control and learning.