A system for robotic heart surgery that learns to tie knots using recurrent neural networks

Hermann Mayer, Faustino Gomez, Daan Wierstra, Istvan Nagy, Alois Knoll, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

76 Scopus citations

Abstract

lying suture knots is a time-consuming task performed frequently during Minimally Invasive Surgery (MIS). Automating this task could greatly reduce total surgery time for patients. Current solutions to this problem replay manually programmed trajectories, but a more general and robust approach is to use supervised machine learning to smooth surgeon-given training trajectories and generalize from them. Since knottying generally requires a controller with internal memory to distinguish between identical inputs that require different actions at different points along a trajectory, it would be impossible to teach the system using traditional feedforward neural nets or support vector machines. Instead we exploit more powerful, recurrent neural networks (RNNs) with adaptive internal states. Results obtained using LSTM RNNs trained by the recent Evolino algorithm show that this approach can significantly increase the efficiency of suture knot tying in MIS over preprogrammed control. © 2006 IEEE.
Original languageEnglish (US)
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Pages543-548
Number of pages6
DOIs
StatePublished - Dec 1 2006
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

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