Arpitha Reddy

University at Buffalo, State University of New York

Papers

Total Citations

H-Index

About

Arpitha Reddy’s research lies at the intersection of surgical robotics and haptic feedback, with a focus on enhancing the safety and precision of robot-assisted procedures. Her most cited work, “A predictive model for haptic assistance in robot assisted trocar insertion” (2013), introduces a novel Prediction from Expert Demonstration (PED) methodology. By collecting force, torque, and penetration trajectory data from expert clinicians, Reddy developed a model that enables robotic systems to anticipate and assist during trocar insertion—a critical step in minimally invasive surgery. This contribution addresses a key challenge in surgical automation: replicating the nuanced tactile judgment of experienced surgeons. Although her citation count (6) reflects a focused, early-career impact, the work’s emphasis on data-driven haptic assistance has informed subsequent advances in shared-control surgical platforms. Reddy’s research demonstrates a commitment to translating clinical expertise into robotic intelligence, offering a pathway toward safer, more intuitive human-robot collaboration in the operating room. Her approach underscores the value of expert demonstration in training predictive models, a methodology with growing relevance in medical robotics and beyond.