Max Robotics — Global Service Robot Directory + US Certification

About

Yunke Ao is a pioneering researcher at the intersection of deep reinforcement learning and surgical robotics, with a focus on enhancing safety and precision in critical medical procedures. His primary research areas include safe reinforcement learning for intraoperative planning, visual-inertial system calibration, and autonomous trajectory optimization. Ao’s most significant contribution is the development of SafeRPlan, a safe deep reinforcement learning framework for pedicle screw placement in spinal fusion surgery. This work addresses the challenge of implanting screws with high accuracy near vital structures under limited anatomical views, achieving 16 citations since 2024 and representing a major advance in robotic surgery safety. He also made notable contributions to visual-inertial calibration, introducing novel model-based heuristic deep reinforcement learning approaches that automate the generation of optimal motion trajectories for sensor calibration, eliminating the need for manual complex routines. His unified data collection framework for visual-inertial calibration further streamlines this process. Ao’s work has been recognized for its potential to improve surgical outcomes and robotic system reliability, positioning him as a rising leader in safe autonomous systems for healthcare.