A learning-based THz multi-layer imaging has been recently used for
contactless three-dimensional (3D) positioning and encoding. We show a
proof-of-concept demonstration of an emerging quantum machine learning (QML)
framework to deal with depth variation, shadow effect, and double-sided content
recognition, through an experimental validation.

本文利用基于学习的 THz 多层成像技术进行非接触三维定位和编码，并展示了新兴量子机器学习 (QML) 框架的概念验证，能够处理深度变化、阴影效应和双面内容识别等问题。

THz 多层成像的量子特征提取

Quantum Feature Extraction for THz Multi-Layer Imaging

For soft continuum arms, visual servoing is a popular control strategy that
relies on visual feedback to close the control loop. However, robust visual
servoing is challenging as it requires reliable feature extraction from the
image, accurate control models and sensors to perceive the shape of the arm,
both of which can be hard to implement in a soft robot. This letter circumvents
these challenges by presenting a deep neural network-based method to perform
smooth and robust 3D positioning tasks on a soft arm by visual servoing using a
camera mounted at the distal end of the arm. A convolutional neural network is
trained to predict the actuations required to achieve the desired pose in a
structured environment. Integrated and modular approaches for estimating the
actuations from the image are proposed and are experimentally compared. A
proportional control law is implemented to reduce the error between the desired
and current image as seen by the camera. The model together with the
proportional feedback control makes the described approach robust to several
variations such as new targets, lighting, loads, and diminution of the soft
arm. Furthermore, the model lends itself to be transferred to a new environment
with minimal effort.

本文提出了一种基于卷积神经网络的方法，针对软臂在结构化环境下实现视觉伺服平稳可靠的 3D 定位，通过神经网络对所需的位置控制器进行预测并使用比例控制法减少误差。