Mikel De Iturrate Reyzabal

About

Mikel De Iturrate Reyzabal is a pioneering researcher at the intersection of soft robotics, surgical automation, and tactile sensing. His primary research areas include physics-based modeling of steerable growing robots, deep-learning force estimation for minimally invasive surgery, and bio-inspired tactile sensing. His most impactful work, "Towards a Physics-Based Model for Steerable Eversion Growing Robots" (2023, 26 citations), introduces a miniature robot capable of navigating fragile environments like human ducts and vessels through eversion growth, with integrated steering and stiffening capabilities—a breakthrough for safe, minimally invasive interventions. In "DaFoEs" (2024, 7 citations), he advances vision-state deep learning for force estimation in robotic surgery, addressing a critical challenge in safe tissue interaction. His comparative study on control methodologies for interventional neuroradiology (2023, 6 citations) evaluates human-robot interfaces for endovascular procedures, while "OMsense" (2023, 4 citations) presents an omnidirectional tactile sensing principle inspired by compound eyes, enabling soft, vision-based tactile feedback. With a growing citation record and contributions spanning modeling, control, and sensing, Reyzabal is shaping the future of intelligent, safe, and adaptive surgical robots.

Research Focus

Key Achievements

Top Papers

1. 1
   Towards a Physics-Based Model for Steerable Eversion Growing Robots

   26 citations · 2023
2. 2
   DaFoEs: Mixing <u>Da</u>tasets Towards the Generalization of Vision-State Deep-Learning <u>Fo</u>rce <u>Es</u>timation in Minimally Invasive Robotic Surgery

   7 citations · 2024
3. 3
   Comparative verification of control methodology for robotic interventional neuroradiology procedures

   6 citations · 2023
4. 4
   OMsense: An Omni Tactile Sensing Principle Inspired by Compound Eyes

   4 citations · 2023

Key Collaborators

Contact & Links