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.

通过从物理机器人收集的数据训练神经网络，我们提出了一个方法来估计手术模拟器中的机器人位置精度误差，并将这些误差注入模拟器中，以产生具有物理机器人特性性能的仿真机器人。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方向差异从 5.0 毫米 / 3.6 度降低到 1.3 毫米 / 1.7 度，分别减少了 3.8 倍和 2.1 倍。

通过注入基于学习的估计误差来提高机器人手术模拟的逼真度

Improving the realism of robotic surgery simulation through injection of  learning-based estimated errors

Spelling correction is the task of identifying spelling mistakes, typos, and
grammatical mistakes in a given text and correcting them according to their
context and grammatical structure. This work introduces "AraSpell," a framework
for Arabic spelling correction using different seq2seq model architectures such
as Recurrent Neural Network (RNN) and Transformer with artificial data
generation for error injection, trained on more than 6.9 Million Arabic
sentences. Thorough experimental studies provide empirical evidence of the
effectiveness of the proposed approach, which achieved 4.8% and 1.11% word
error rate (WER) and character error rate (CER), respectively, in comparison
with labeled data of 29.72% WER and 5.03% CER. Our approach achieved 2.9% CER
and 10.65% WER in comparison with labeled data of 10.02% CER and 50.94% WER.
Both of these results are obtained on a test set of 100K sentences.

使用不同的 seq2seq 模型架构（如递归神经网络（RNN）和 Transformer）和人工数据生成进行错误注入，本研究介绍了一种用于阿拉伯语拼写纠正的框架 AraSpell，并通过彻底的实验研究提供了所提方法的有效性的经验论据，其与标记数据相比，词错误率（WER）和字符错误率（CER）分别达到了 4.8% 和 1.11%，与标记数据相比，CER 分别为 2.9%，WER 为 10.65%。这些结果都是在一个包含 100K 个句子的测试集上获得的。