Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks such as suturing can lead to surgeon fatigue, motivating the automation of suturing. As visual tracking of a thin reflective needle is extremely challenging, prior work has modified the needle with nonreflective contrasting paint. As a step towards automation of a suturing subtask without modifying the needle, we propose HOUSTON: Handoff of Unmodified, Surgical, Tool-Obstructed Needles, a problem and algorithm that uses a learned active sensing policy with a stereo camera to localize and align the needle into a visible and accessible pose for the other arm. To compensate for robot positioning and needle perception errors, the algorithm then executes a high-precision grasping motion that uses multiple cameras. In physical experiments using the da Vinci Research Kit (dVRK), HOUSTON successfully passes unmodified surgical needles with a success rate of 96.7% and is able to perform handover sequentially between the arms 32.4 times on average before failure. On needles unseen in training, HOUSTON achieves a success rate of 75 - 92.9%. To our knowledge, this work is the first to study handover of unmodified surgical needles. See https://tinyurl.com/houston-surgery for additional materials.

翻译：专家外科医生通常使用机械机械外科助理(RSAs)来进行最低侵入性手术。然而,由陈腐和重复性任务(如涂鸦等)填满的冗长程序会导致外科疲劳,促使自压自动化。由于对细反射针的视觉跟踪极具挑战性,先前的工作已经用非反射对比涂料对针头进行了高精度的涂料。作为向自压下层任务自动化迈出的一步,我们提议HOUSTON:手动未经修改、超常、工具-工具-工具-工具-不规则,这是一个问题和算法,它使用一种用立体照相机将针植入本地化,并将针头调整成另一臂的可见易懂和易懂的姿势。为了弥补机器人定位和针头感知误差,该算法随后用多种照相机对针头进行高精度的抓动。在实际实验中,使用达芬奇研究包(dVRK)成功率达96.7%的HOUSTON成功地传递了更多的未变手术针头,并且能够连续在武器之间进行交接转动,32.9.9次,在前将HHS-HSI-SLLUTHS-S-SUTHSUDLLLLM 上的成功率率前完成75试验成功率前的试验。