Surgi-HDTMR: Closing the Sensorimotor Loop in Bimanual Microsurgery via Haptics, Digital Twin, and Mixed Reality
This addresses the need for safer and more efficient skill acquisition in robot-assisted microsurgery training, representing an incremental improvement with specific gains.
The paper tackled the problem of robotic microsurgery training lacking immersive 3D interaction and high-fidelity haptics by presenting Surgi-HDTMR, a mixed-reality and digital-twin system with depth-adaptive haptics, which in a study shortened task time, reduced harmful contacts, and improved perceptual accuracy compared to baselines.
Robotic microsurgery demands precise bimanual control, intuitive interaction, and informative force feedback. However, most training platforms for robotic microsurgery lack immersive 3D interaction and high-fidelity haptics. Here, we present Surgi-HDTMR, a mixed-reality (MR) and digital-twin (DT) training system that couples bimanual haptic teleoperation with a benchtop microsurgical robotic platform, and 3D-printed phantoms. A metrically co-registered, time-synchronized DT aligns in-situ MR guidance with the physical workspace and drives a depth-adaptive haptic model that renders contact, puncture, and tissue-retraction forces. In a within-subjects study of simulated cortical navigation and tumor resection, Surgi-HDTMR shortened task time, reduced harmful contacts and collisions, and improved perceptual accuracy relative to non-haptic and non-adaptive baselines. These results suggest that tightly coupling MR overlays with a synchronized DT, together with depth-adaptive haptics, can accelerate skill acquisition and improve safety in robot-assisted microsurgery, pointing toward next-generation surgical training.