Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following

Xucheng Wang, Zhizhou Yang, Xiaoman Zhang, Sung Eun Kim, Romain Hardy, Pranav Rajpurkar
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:2131-2150, 2026.

Abstract

This study presents the first evaluation of general-purpose imitation learning for surgeon-robot collaborative assistance in open surgery, targeting suture following: the grab-pull-release motion an assistant performs at every stitch. We collect 160 teleoperated demonstrations (32,374 frames) on an open-source robot arm, benchmark four architecturally diverse imitation learning policies (ACT, Diffusion Policy, SmolVLA, $\pi_0$) across 28 trained models evaluated in 32 configurations along three clinically motivated dimensions: dataset size, camera viewpoint, and background variation. Our results demonstrate that under ideal conditions, the four policies achieve 50–75% task success, with depth error as the dominant failure mode across all architectures. Among all policies, $\pi_0$ achieves the strongest results with a pretrained vision-language backbone, demonstrating superior data efficiency, greater robustness to background variation, and smoother trajectories compatible with surgical workflow. When deployed in a surgeon-robot suturing trial, $\pi_0$ yields a 92% stitch completion rate. These findings establish collaborative robotic assistance in open surgery as a feasible target for imitation learning and highlight depth perception and end-effector design as key priorities for clinical translation. All code, data, and model weights are released at https://github.com/rajpurkarlab/Suture_Follow_Evaluation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-wang26b, title = {Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following}, author = {Wang, Xucheng and Yang, Zhizhou and Zhang, Xiaoman and Kim, Sung Eun and Hardy, Romain and Rajpurkar, Pranav}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {2131--2150}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/wang26b/wang26b.pdf}, url = {https://proceedings.mlr.press/v340/wang26b.html}, abstract = {This study presents the first evaluation of general-purpose imitation learning for surgeon-robot collaborative assistance in open surgery, targeting suture following: the grab-pull-release motion an assistant performs at every stitch. We collect 160 teleoperated demonstrations (32,374 frames) on an open-source robot arm, benchmark four architecturally diverse imitation learning policies (ACT, Diffusion Policy, SmolVLA, $\pi_0$) across 28 trained models evaluated in 32 configurations along three clinically motivated dimensions: dataset size, camera viewpoint, and background variation. Our results demonstrate that under ideal conditions, the four policies achieve 50–75% task success, with depth error as the dominant failure mode across all architectures. Among all policies, $\pi_0$ achieves the strongest results with a pretrained vision-language backbone, demonstrating superior data efficiency, greater robustness to background variation, and smoother trajectories compatible with surgical workflow. When deployed in a surgeon-robot suturing trial, $\pi_0$ yields a 92% stitch completion rate. These findings establish collaborative robotic assistance in open surgery as a feasible target for imitation learning and highlight depth perception and end-effector design as key priorities for clinical translation. All code, data, and model weights are released at https://github.com/rajpurkarlab/Suture_Follow_Evaluation.} }
Endnote
%0 Conference Paper %T Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following %A Xucheng Wang %A Zhizhou Yang %A Xiaoman Zhang %A Sung Eun Kim %A Romain Hardy %A Pranav Rajpurkar %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-wang26b %I PMLR %P 2131--2150 %U https://proceedings.mlr.press/v340/wang26b.html %V 340 %X This study presents the first evaluation of general-purpose imitation learning for surgeon-robot collaborative assistance in open surgery, targeting suture following: the grab-pull-release motion an assistant performs at every stitch. We collect 160 teleoperated demonstrations (32,374 frames) on an open-source robot arm, benchmark four architecturally diverse imitation learning policies (ACT, Diffusion Policy, SmolVLA, $\pi_0$) across 28 trained models evaluated in 32 configurations along three clinically motivated dimensions: dataset size, camera viewpoint, and background variation. Our results demonstrate that under ideal conditions, the four policies achieve 50–75% task success, with depth error as the dominant failure mode across all architectures. Among all policies, $\pi_0$ achieves the strongest results with a pretrained vision-language backbone, demonstrating superior data efficiency, greater robustness to background variation, and smoother trajectories compatible with surgical workflow. When deployed in a surgeon-robot suturing trial, $\pi_0$ yields a 92% stitch completion rate. These findings establish collaborative robotic assistance in open surgery as a feasible target for imitation learning and highlight depth perception and end-effector design as key priorities for clinical translation. All code, data, and model weights are released at https://github.com/rajpurkarlab/Suture_Follow_Evaluation.
APA
Wang, X., Yang, Z., Zhang, X., Kim, S.E., Hardy, R. & Rajpurkar, P.. (2026). Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:2131-2150 Available from https://proceedings.mlr.press/v340/wang26b.html.

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