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About

Andrea Roberti is a robotics researcher whose work spans two interconnected domains: autonomous surgical robotics and robot motion planning. His most influential contribution lies in advancing task automation for minimally invasive surgery, where he has developed knowledge-based frameworks, statechart-based procedure modeling, and situation-aware planning systems that enable robots to reason about surgical environments and execute structured tasks autonomously — work that has collectively garnered over 80 citations. Alongside this surgical focus, Roberti has made significant strides in robot motion planning, most notably through his highly cited work on Dynamic Movement Primitives with volumetric obstacle avoidance using dynamic potential functions (76 citations), which addressed the long-standing challenge of representing and navigating around three-dimensional obstacles in real time. His research also extends into active object recognition, 3D scene reconstruction, and novel endoscope design for anatomical visualization, reflecting a broad systems-level perspective on surgical robotics. Particularly noteworthy is his consistent effort to bridge data-driven and knowledge-driven approaches — merging machine learning with formal task modeling — to create scalable, intelligent robotic systems capable of operating reliably in complex, safety-critical environments.