Performance-Driven Tasks with Adaptive Difficulty for Enhanced Surgical Robotics Training

Surgical robotics training most often occurs through standardized curricula and exercises that lack customization and do not adapt to the different levels of proficiency that trainees often present. This work proposes a Virtual Reality (VR) simulator for surgical robotics that autonomously adjusts difficulty levels based on trainee performance, aiming to enhance skill retention and transfer. The study employs a performance-based adaptive difficulty approach, dynamically adjusting parameters of each task's morphology to match individual proficiency levels. The proposed adaptive simulator is evaluated against a non-adaptive counterpart through a week-long training program. The results demonstrate the effectiveness of the adaptive simulator in enhancing performance at higher difficulty levels, supporting its potential to benefit surgical education by providing a tailored and scalable training approach.