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, $π_0$) 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, $π_0$ 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, $π_0$ 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.

翻译：本研究首次评估了通用模仿学习在开放性手术中用于外科医生-机器人协作辅助的表现，聚焦于缝线跟随任务：即助手在每针操作中执行的抓取-牵引-释放动作。我们基于开源机器人臂收集了160次遥操作演示（共32,374帧），对四种架构差异化的模仿学习策略（ACT、扩散策略、SmolVLA、$π_0$）进行了基准测试，涵盖28个训练模型，并在32种配置下沿三个临床相关维度（数据集规模、相机视角、背景变化）进行评估。结果表明，在理想条件下，四种策略的任务成功率为$50$-$75\%$，深度误差是所有架构中的主要失败模式。在所有策略中，$π_0$凭借其预训练的视觉-语言骨干网络取得了最佳结果，展现出更高的数据效率、更强的背景变化鲁棒性以及与手术工作流程兼容的更平滑轨迹。在外科医生-机器人缝合试验部署中，$π_0$实现了$92\%$的缝针完成率。这些发现表明，开放式手术中的协作式机器人辅助是模仿学习的可行目标，同时强调深度感知和末端执行器设计是临床转化的关键优先方向。