Autonomous Bi-Manual Surgical Suturing Based on Skills Learned from Demonstration

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Abstract

We present a novel application of Learning from Demonstration to realize a fully autonomous bi-manual surgical suturing task, including needle pick up, insertion, re-grasping, extraction and hand-over. Surgical action primitives are learned from a single human demonstration and encoded into an action library from which they are pulled to compose more elaborate tasks at planning/execution time. The method is demonstrated in a non-clinical setting, using unmodified surgical instruments with a custom surgical robot system. We use stereo vision to automatically detect the suture needle and entry points to close the control loop and generalize tasks to different task conditions. The suturing task is shown to generalize well to differing initial conditions with a success rate of 17% for the full task, a mean subtask success rate of 75% and mean needle insertion error of 3.3 mm over the course of 46 trial task executions at human speed. Failures could all be attributed to erroneous vision-based detection, pose estimation and robot calibration.
OriginalsprogEngelsk
Titel2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
ForlagIEEE
Publikationsdato2021
Sider4017-4024
ISBN (Elektronisk)978-1-6654-1714-3
DOI
StatusUdgivet - 2021
Begivenhed2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Online, Prague, Tjekkiet
Varighed: 27. sep. 20211. okt. 2021
Konferencens nummer: 34
https://www.iros2021.org/

Konference

Konference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Nummer34
LokationOnline
Land/OmrådeTjekkiet
ByPrague
Periode27/09/202101/10/2021
Internetadresse
NavnI E E E International Conference on Intelligent Robots and Systems. Proceedings
ISSN2153-0858

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