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Antonio Visioli is a prominent researcher in robotics and automation, whose work has significantly advanced the fields of robot trajectory planning, motion control, and force/velocity interaction. Best known for his foundational 2000 paper "Global Minimum-Jerk Trajectory Planning of Robot Manipulators" — now cited over 369 times — Visioli pioneered the use of interval analysis and cubic splines to generate smooth, human-like robot motions that minimize vibration and improve path tracking. His subsequent contributions refined these methods through algebraic, trigonometric, and B-spline techniques, establishing a comprehensive toolkit for real-time trajectory modification in industrial settings. Beyond trajectory planning, Visioli has made substantial contributions to robot control, including hybrid force/velocity control for contour tracking, friction modeling with temperature compensation, and iterative learning control strategies for unknown surface following. His 2019 work on predictive inverse kinematics demonstrates a continued evolution toward constraint-aware, real-time solutions for redundant manipulators. Practical innovation is a hallmark of his research, as evidenced by his development of a virtual force sensor enabling interaction tasks on conventional industrial robots without dedicated hardware. Collectively, his body of work — spanning experimental validation on SCARA and industrial manipulators — has had enduring impact on both academic research and real-world robotics applications.