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About

Marco Laghi is a robotics researcher whose work sits at the forefront of teleoperation, shared autonomy, and human-robot interaction. His research addresses one of the field's central challenges: enabling humans to intuitively and efficiently control complex robotic systems — particularly multi-arm manipulators — in demanding remote environments. Laghi's most influential contribution, "Shared-Autonomy Control for Intuitive Bimanual Tele-Manipulation" (2018, 55 citations), introduced a paradigm shift away from rigid one-to-one human-robot arm coupling, demonstrating how intelligent autonomy allocation can dramatically improve bimanual task performance. This foundational work inspired a suite of follow-on studies exploring reconfigurable control frameworks, assisted grasping architectures, and operator ergonomics — notably his 2020 paper on musculoskeletal-model-driven arm posture optimization during bilateral teleoperation. His research also spans haptic feedback systems, tele-impedance under communication delays, and learning from demonstration, bridging the gap between teleoperation data and autonomous robot skill acquisition. More recently, he has explored reinforcement learning for adaptive task-priority management in unstructured manufacturing settings. Collectively accumulating over 140 citations, Laghi's body of work makes him a notable contributor to the design of safer, more natural, and increasingly intelligent human-robot collaborative systems.