Ahmad Sharifi

University of Illinois Chicago, K.N.Toosi University of Technology

Papers

Total Citations

H-Index

About

Ahmad Sharifi is a robotics researcher focused on advancing human-robot interaction and intelligent control systems. His work spans three key areas: surgical robotics, cable-driven parallel robots (CDPRs), and deep reinforcement learning (DRL). In 2025, Sharifi introduced a **Sensory Glove-Based Surgical Robot User Interface**, a novel approach to replace bulky surgeon consoles with intuitive, wearable control—addressing critical issues of space, team coordination, and proprietary limitations in the operating room. He has also made significant contributions to CDPR control, notably implementing **Deep Reinforcement Learning** (Deep Deterministic Policy Gradient) to overcome challenges of cable dynamics and environmental uncertainty, achieving precise, adaptive control. Earlier, Sharifi developed an **online time-optimal trajectory planning** method for cable-suspended robots, incorporating real-time cable force constraints to ensure safe and efficient motion in dynamic workspaces. While his most-cited papers currently hold 2 citations each, they represent foundational work in emerging, high-impact areas. Sharifi’s research bridges the gap between theoretical control methods and practical, user-centered robotic systems, positioning him as a promising innovator in next-generation surgical and industrial robotics.