This paper introduces an end-to-end fine-tuning method to improve hand-eye
coordination in modular deep visuo-motor policies (modular networks) where each
module is trained independently. Benefiting from weighted losses, the
fine-tuning method significantly improves the performance of the policies for a
robotic planar reaching task.

本篇论文介绍了一种端到端的微调方法，用于提高模块化深度视觉运动策略（模块网络）中的手眼协调能力。通过加权损失，微调方法显著提高了机器人平面够到任务的性能。

利用加权损失函数对模块化网络进行调整实现手眼协调

Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination

We describe a learning-based approach to hand-eye coordination for robotic
grasping from monocular images. To learn hand-eye coordination for grasping, we
trained a large convolutional neural network to predict the probability that
task-space motion of the gripper will result in successful grasps, using only
monocular camera images and independently of camera calibration or the current
robot pose. This requires the network to observe the spatial relationship
between the gripper and objects in the scene, thus learning hand-eye
coordination. We then use this network to servo the gripper in real time to
achieve successful grasps. To train our network, we collected over 800,000
grasp attempts over the course of two months, using between 6 and 14 robotic
manipulators at any given time, with differences in camera placement and
hardware. Our experimental evaluation demonstrates that our method achieves
effective real-time control, can successfully grasp novel objects, and corrects
mistakes by continuous servoing.

通过卷积神经网络对单目图像进行学习，实现机器人抓握的手眼协调，并能实现实时控制、成功抓握陌生物体，以及通过连续伺服调节校正抓握错误。