Transfer of learned dynamics between different surgical robots and operative configurations - TÜMERDEM, UĞUR (My University, 2022)
Purpose Using the da Vinci Research Kit (dVRK), we propose and experimentally demonstrate transfer learning (Xfer) of dynamics between different configurations and robots distributed around the world. This can extend recent research using neural networks to estimate the dynamics of the patient side manipulator (PSM) to provide accurate external end-effector force estimation, by adapting it to different robots and instruments, and in different configurations, with additional forces applied on the instruments as they pass through the trocar. Methods The goal of the learned models is to predict internal joint torques during robot motion. First, exhaustive training is performed during free-space (FS) motion, using several configurations to include gravity effects. Second, to adapt to different setups, a limited amount of training data is collected and then the neural network is updated through Xfer. Results Xfer can adapt a FS network trained on one robot, in one configuration, with a particular instrument, to provide comparable joint torque estimation for a different robot, in a different configuration, using a different instrument, and inserted through a trocar. The robustness of this approach is demonstrated with multiple PSMs (sampled from the dVRK community), instruments, configurations and trocar ports. Conclusion Xfer provides significant improvements in prediction errors without the need for complete training from scratch and is robust over a wide range of robots, kinematic configurations, surgical instruments, and patient-specific setups.
Keywords: Tıp; Sağlık Bilimleri; Dahili Tıp Bilimleri; Nükleer Tıp; Cerrahi Tıp Bilimleri; Biyomedikal Mühendisliği; Mühendislik ve Teknoloji; Medicine; Health Sciences; Internal Medicine Sciences; Nuclear medicine; Surgery Medicine Sciences; Biomedical Engineering; Engineering and Technology; MÜHENDİSLİK, BİYOMEDİKAL; Mühendislik; Mühendislik, Bilişim ve Teknoloji (ENG); RADYOLOJİ, NÜKLEER TIP ve MEDİKAL GÖRÜNTÜLEME; Klinik Tıp; Klinik Tıp (MED); CERRAHİ; ENGINEERING, BIOMEDICAL; ENGINEERING; Engineering, Computing & Technology (ENG); RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING; CLINICAL MEDICINE; Clinical Medicine (MED); SURGERY; General Engineering; Engineering (miscellaneous); Bioengineering; Radiology, Nuclear Medicine and Imaging; Surgery; Radiological and Ultrasound Technology; Physical Sciences; Surgical robotics; Dynamic identification; Tactile sensing; Transfer learning; ADAPTIVE-CONTROL; NEURAL-NETWORKS; IDENTIFICATION