Gojko Perovic

Piaggio (Italy), Scuola Superiore Sant'Anna

Papers

Total Citations

H-Index

About

Gojko Perovic is a rising leader in human-robot interaction, specializing in the nuanced challenge of physical handovers between robots and humans. His research centers on developing adaptive, reactive robotic systems that can seamlessly collaborate with people in dynamic environments. Perovic’s major contribution lies in integrating Dynamic Movement Primitives (DMPs) with Preference Learning (PL) to generate online, human-aware trajectories. His most-cited work, “DMP-Based Reactive Robot-to-Human Handover in Perturbed Scenarios” (2023, 16 citations), demonstrates how robots can adjust their motion in real-time to accommodate human partners, even during unexpected interruptions. This foundational paper has quickly become a reference point for researchers tackling the complexities of physical human-robot collaboration. In his follow-up work, “Adaptive Robot-Human Handovers With Preference Learning” (2023, 4 citations), Perovic further refines this approach by enabling robots to learn and adapt to individual user preferences, modulating speed and trajectory for more natural interactions. Through these contributions, Perovic is helping to move robots from rigid, pre-programmed tools to intuitive, collaborative partners, with significant implications for manufacturing, healthcare, and assistive robotics.