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

Reinforcement learning shows great potential to solve complex contact-rich
robot manipulation tasks. However, the safety of using RL in the real world is
a crucial problem, since unexpected dangerous collisions might happen when the
RL policy is imperfect during training or in unseen scenarios. In this paper,
we propose a contact-safe reinforcement learning framework for contact-rich
robot manipulation, which maintains safety in both the task space and joint
space. When the RL policy causes unexpected collisions between the robot arm
and the environment, our framework is able to immediately detect the collision
and ensure the contact force to be small. Furthermore, the end-effector is
enforced to perform contact-rich tasks compliantly, while keeping robust to
external disturbances. We train the RL policy in simulation and transfer it to
the real robot. Real world experiments on robot wiping tasks show that our
method is able to keep the contact force small both in task space and joint
space even when the policy is under unseen scenario with unexpected collision,
while rejecting the disturbances on the main task.

本文提出一种安全的强化学习框架，用于处理复杂的接触丰富的机器人操作任务，能够在任务空间和关节空间中保持安全，同时使机械臂与环境发生的接触力保持较小。该框架采用模拟训练，成功地在实际机器人上验证了其性能。