In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.

翻译：在手术中，施加适当的力量水平对于手术的成功和安全至关重要。尽管许多研究聚焦于原位力的测量，但鲜有关注如何将这些观察到的力与手术技术相关联。回答诸如“改变手术技术的某些方面能否降低作用力并提高安全裕度？”这类问题，有望改进手术实践，并最终改善患者预后。然而，此类研究需要大量试验和执业外科医生的参与，这在实际操作中通常难以实现。我们转而证明，机器人如何通过学习少量手术演示中的多种手术技术变体，并通过参数化技能模型对习得行为进行插值。这使机器人系统能够执行大量试验，并分析手术技术及其对组织的下游影响。本文提出一种椭圆切口切除技能的参数化模型，并应用贝叶斯优化方案，根据专家评分以及切除力的个体特征来优化切除行为。结果表明，所提出的框架能够成功地将生成的机器人行为与不同熟练程度受试者在切除力特征上对齐。