Alessio Sozzi
Papers
9
Total Citations
142
H-Index
7
About
Alessio Sozzi is a robotics researcher whose work sits at the intersection of surgical robotics, autonomous systems, and human-robot collaboration. His research focuses primarily on developing intelligent architectures and motion planning frameworks for robotic systems operating in high-stakes medical environments, particularly robotic minimally invasive surgery (R-MIS). Among his most significant contributions is the development of cognitive robotic architectures that enable semi-autonomous execution of surgical tasks, integrating perception, decision-making, and control into cohesive systems capable of assisting surgeons in real time. His work on dynamic motion planning and Model Predictive Control (MPC) has advanced the ability of surgical robots to generate collision-free trajectories in dynamic, cluttered environments — a critical safety requirement in operating rooms. His multi-modal learning system for surgical action segmentation, cited 29 times, demonstrates his commitment to combining machine learning with deterministic robotics to ensure reliable autonomy. Beyond surgery, Sozzi has extended his expertise to assistive robotics, notably designing a wheelchair-mounted robotic arm to support upper-limb impaired patients. With a body of work accumulating over 140 citations, his research consistently bridges theoretical rigor with real-world clinical applicability, making meaningful contributions to the future of autonomous and assistive medical robotics.
Research Focus
Key Achievements
Top Papers
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- 3Dynamic Motion Planning for Autonomous Assistive Surgical Robots25 citations · 2019
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- 9Linear MPC-based Motion Planning for Autonomous Surgery2 citations · 2022