Mikel De Iturrate Reyzabal — accepted AI-conference papers

2024

DaFoEs: Mixing Datasets Towards the Generalization of Vision-State Deep-Learning Force Estimation in Minimally Invasive Robotic Surgery

Mikel De Iturrate Reyzabal, Mingcong Chen, Wei Huang, Sébastien Ourselin, Hongbin Liu

RA-L 2024

Precisely determining the contact force during safe interaction in Minimally Invasive Robotic Surgery (MIRS) is still an open research challenge. Inspired by post-operative qualitative analysis from surgical videos, the use of cross-modality data driven deep neural network models has been one of the

Cited by 7 Source ↗code ↗Scholar ↗

2023

Towards a Physics-Based Model for Steerable Eversion Growing Robots

Zicong Wu, Mikel De Iturrate Reyzabal, S. M. Hadi Sadati, Hongbin Liu, Sébastien Ourselin, Daniel Richard Leff +3

RA-L 2023

Soft robots that grow through eversion/apical extension can effectively navigate fragile environments such as ducts and vessels inside the human body. This paper presents the physics-based model of a miniature steerable eversion growing robot. We demonstrate the robot's growing, steering, stiffening

Cited by 19 Source ↗Scholar ↗