通过注入基于学习估计的误差来提高机器人手术模拟的真实感
Improving the realism of robotic surgery simulation through injection of learning-based estimated errors
AI总结：
针对手术模拟器中机器人位置精度过于理想化的问题，提出用物理机器人数据训练神经网络估计控制器及运动学、非运动学误差并注入模拟器，使位置和姿态差异分别降至1.3 mm和1.7度。
摘要：
机器人手术期间子任务自动化算法的开发可借助真实感模拟环境而得到加速。在本工作中，我们关注手术模拟器真实感的一个方面，即机器人的位置精度。在当前模拟器中，机器人具有完美或接近完美的精度，这并不能代表其物理对应物。因此，我们提出一对神经网络，利用从物理机器人收集的数据进行训练，以估计控制器误差以及运动学和非运动学误差。随后，这些误差估计被注入模拟器中，以生成具有物理机器人特征性能的模拟机器人。在这种情况下，我们认为，模拟中使用的估计误差只要与物理机器人的实际误差具有统计上相似的分布就足够了。与物理机器人误差补偿要求估计误差应等于实际误差相比，这一要求较宽松，因此也更站得住脚。我们的结果表明，误差注入将模拟机器人与物理机器人之间的平均位置和姿态差异分别从5.0 mm / 3.6度降低到1.3 mm / 1.7度，分别降低了3.8倍和2.1倍。
英文摘要：
The development of algorithms for automation of subtasks during robotic surgery can be accelerated by the availability of realistic simulation environments. In this work, we focus on one aspect of the realism of a surgical simulator, which is the positional accuracy of the robot. In current simulators, robots have perfect or near-perfect accuracy, which is not representative of their physical counterparts. We therefore propose a pair of neural networks, trained by data collected from a physical robot, to estimate both the controller error and the kinematic and non-kinematic error. These error estimates are then injected within the simulator to produce a simulated robot that has the characteristic performance of the physical robot. In this scenario, we believe it is sufficient for the estimated error used in the simulation to have a statistically similar distribution to the actual error of the physical robot. This is less stringent, and therefore more tenable, than the requirement for error compensation of a physical robot, where the estimated error should equal the actual error. Our results demonstrate that error injection reduces the mean position and orientation differences between the simulated and physical robots from 5.0 mm / 3.6 deg to 1.3 mm / 1.7 deg, respectively, which represents reductions by factors of 3.8 and 2.1.
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