Autonomous Needle Manipulation for Robotic Surgical Suturing Based on Skills Learned from Demonstration
Autonomous Needle Manipulation for Robotic Surgical Suturing Based on Skills Learned from Demonstration
复制标题
基于演示技能的机器人手术缝合自主针操作
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
T. Savarimuthu
中科院分区：
文献类型：
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作者：
K. Schwaner;D. Dall’Alba;P. T. Jensen;P. Fiorini;T. Savarimuthu
在未来，手术机器人将允许在外科医生的监督下自主执行手术任务。我们提出了一个简单的框架，用于学习手术动作原语，可以用作构建块组成更精细的手术任务。我们的方法是基于从演示中学习（LfD），因为这使我们能够利用现有的专家知识，从记录的外科手术。我们证明，我们可以学习针操作动作，从人类示范，构建一个动作库，用于自主执行的一部分，外科手术的任务。动作是从单个演示中学习的，我们使用动态运动原语（DMPs）来编码低级别的笛卡尔空间轨迹。我们的方法在非临床环境中进行了实验验证，在那里我们表明，学习的动作可以推广到以前看不见的条件。实验表明，对于从演示的初始条件的适度变化，任务成功率为81%，平均针插入误差为3.8 mm。
In the future, surgical robots will grant the option of executing surgical tasks autonomously, supervised by the surgeon. We propose a simple framework for learning surgical action primitives that can be used as building blocks for composing more elaborate surgical tasks. Our method is based on Learning from Demonstration (LfD) as this allows us to exploit existing expert knowledge from recordings of surgical procedures. We demonstrate that we can learn needle manipulation actions from human demonstration, constructing an action library which is used to autonomously execute part of a surgical suturing task. Actions are learned from single demonstrations and we use Dynamic Movement Primitives (DMPs) to encode low-level Cartesian space trajectories. Our method is experimentally validated in a non-clinical setting, where we show that learned actions can be generalized to previously unseen conditions. Experiments show a 81 % task success rate for moderate variations from the initial conditions of the demonstration with a mean needle insertion error of 3.8 mm.