@inproceedings{2cefd67fb9f5442d98f48d459a60cd6c,
title = "A Handheld Forceps-Like Tracker for Learning Robotic Surgical Tasks from Demonstration",
abstract = "This paper presents a novel approach to collecting demonstrations of surgical procedures that can be used to transfer skills from experts to robots using standard learning from demonstration. We use a handheld forceps-like magnetic tracker similar to the grasper of an Intuitive Surgical Inc. Large Needle Driver to record the demonstrations. Our approach does not depend on a surgical robot for the demonstration phase. We show that the resulting demonstrations can be executed on a surgical robot system by encoding them as Dynamic Movement Primitives. Our evaluation shows that users are quicker at demonstrating trajectories with the device rather than by teleoperating the robot, at the expense of slightly more noise due to hand tremors.",
keywords = "autonomy in robotic surgery, learning from demonstration, robot-assisted minimally invasive surgery",
author = "I{\~n}igo Iturrate and Schwaner, {Kim Lindberg} and Savarimuthu, {Thiusius R.} and Zhuoqi Cheng",
year = "2023",
doi = "10.1007/978-3-031-32606-6_27",
language = "English",
isbn = "978-3-031-32605-9",
series = "Mechanisms and Machine Science",
publisher = "Springer",
pages = "229--236",
editor = "Petri{\v c}, { Tadej} and Ude, { Ale{\v s}} and {Leon {\v Z}lajpah}, Leon",
booktitle = "Advances in Service and Industrial Robotics",
address = "Germany",
note = "32nd International Conference on Robotics in Alpe-Adria-Danube Region, RAAD ; Conference date: 14-06-2023 Through 16-06-2023",
}