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, π0) 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, π0 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, π0 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.
@article{arxiv.2605.28736,
title = {Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following},
author = {Xucheng Wang and Zhizhou Yang and Xiaoman Zhang and Sung Eun Kim and Romain Hardy and Pranav Rajpurkar},
journal= {arXiv preprint arXiv:2605.28736},
year = {2026}
}