In this study, we further investigate the robustness and generalization
ability of an neural network (NN) based force estimation method, using the da
Vinci Research Kit Si (dVRK-Si). To evaluate our method's performance, we
compare the force estimation accuracy with several baseline methods. We conduct
comparative studies between the dVRK classic and dVRK-Si systems to benchmark
the effectiveness of these approaches.
We conclude that the NN-based method provides comparable force estimation
accuracy across the two systems, as the average root mean square error (RMSE)
over the average range of force ratio is approximately 3.07% for the dVRK
classic, and 5.27% for the dVRK-Si. On the dVRK-Si, the force estimation RMSEs
for all the baseline methods are 2 to 4 times larger than the NN-based method
in all directions. One possible reason is, we made assumptions in the baseline
methods that static forces remain the same or dynamics is time-invariant. These
assumptions may hold for the dVRK Classic, as it has pre-loaded weight and
maintains horizontal self balance. Since the dVRK-Si configuration does not
have this property, assumptions do not hold anymore, therefore the NN-based
method significantly outperforms.

通过使用 dVRK-Si，本研究进一步研究了基于神经网络的力量估计方法的鲁棒性和泛化能力，并将其性能与几种基准方法进行了比较，结果表明神经网络方法在两个系统上提供了可比较的力量估计准确性。

基线和神经网络力量估计方法对达芬奇研究套件 Si 系统的有效性研究

An Effectiveness Study Across Baseline and Neural Network-based Force  Estimation Methods on the da Vinci Research Kit Si System

Precisely determining the contact force during safe interaction in Minimally
Invasive Robotic Surgery (MIRS) is still an open research challenge. Inspired
by post-operative qualitative analysis from surgical videos, the use of
cross-modality data driven deep neural network models has been one of the
newest approaches to predict sensorless force trends. However, these methods
required for large and variable datasets which are not currently available. In
this paper, we present a new vision-haptic dataset (DaFoEs) with variable soft
environments for the training of deep neural models. In order to reduce the
bias from a single dataset, we present a pipeline to generalize different
vision and state data inputs for mixed dataset training, using a previously
validated dataset with different setup. Finally, we present a variable
encoder-decoder architecture to predict the forces done by the laparoscopic
tool using single input or sequence of inputs. For input sequence, we use a
recurrent decoder, named with the prefix R, and a new temporal sampling to
represent the acceleration of the tool. During our training, we demonstrate
that single dataset training tends to overfit to the training data domain, but
has difficulties on translating the results across new domains. However,
dataset mixing presents a good translation with a mean relative estimated force
error of 5% and 12% for the recurrent and non-recurrent models respectively.
Our method, also marginally increase the effectiveness of transformers for
force estimation up to a maximum of ~15%, as the volume of available data is
increase by 150%. In conclusion, we demonstrate that mixing experimental set
ups for vision-state force estimation in MIRS is a possible approach towards
the general solution of the problem.

通过创建新的视触觉数据集，使用深度神经网络模型进行训练，并将不同的视觉和状态数据输入泛化为混合数据集训练的通用管线，该研究展示了在微创机器人手术中，混合实验设置用于视觉状态力估计是解决问题的一个可行方法。

DaFoEs：混合数据集以实现微创机器人手术视觉状态深度学习力估计的泛化

DaFoEs: Mixing Datasets towards the generalization of vision-state  deep-learning Force Estimation in Minimally Invasive Robotic Surgery

Seeing-eye robots are very useful tools for guiding visually impaired people,
potentially producing a huge societal impact given the low availability and
high cost of real guide dogs. Although a few seeing-eye robot systems have
already been demonstrated, none considered external tugs from humans, which
frequently occur in a real guide dog setting. In this paper, we simultaneously
train a locomotion controller that is robust to external tugging forces via
Reinforcement Learning (RL), and an external force estimator via supervised
learning. The controller ensures stable walking, and the force estimator
enables the robot to respond to the external forces from the human. These
forces are used to guide the robot to the global goal, which is unknown to the
robot, while the robot guides the human around nearby obstacles via a local
planner. Experimental results in simulation and on hardware show that our
controller is robust to external forces, and our seeing-eye system can
accurately detect force direction. We demonstrate our full seeing-eye robot
system on a real quadruped robot with a blindfolded human. The video can be
seen at our project page: this https URL

通过强化学习 (RL) 和监督学习同时训练具有抗外部牵引力的运动控制器以及外力估计器，从而实现稳定行走和对人类外部力的响应，进而引导机器人达到未知的全局目标，并通过局部规划器引导人类绕过附近的障碍物，实验结果表明我们的控制器能够抵御外部力，我们的盲人导盲机器人系统能够准确检测外力方向。