In the realm of robot action recognition, identifying distinct but spatially
proximate arm movements using vision systems in noisy environments poses a
significant challenge. This paper studies robot arm action recognition in noisy
environments using machine learning techniques. Specifically, a vision system
is used to track the robot's movements followed by a deep learning model to
extract the arm's key points. Through a comparative analysis of machine
learning methods, the effectiveness and robustness of this model are assessed
in noisy environments. A case study was conducted using the Tic-Tac-Toe game in
a 3-by-3 grid environment, where the focus is to accurately identify the
actions of the arms in selecting specific locations within this constrained
environment. Experimental results show that our approach can achieve precise
key point detection and action classification despite the addition of noise and
uncertainties to the dataset.

在嘈杂的环境中，通过使用机器学习技术和视觉系统来识别机器人手臂的动作是一个重要挑战。本文通过比较分析机器学习方法，研究了机器人手臂在嘈杂环境中的动作识别，并评估了该模型在噪声环境中的有效性和鲁棒性。通过在一个 3x3 网格环境中进行的井字棋案例研究，重点是准确识别手臂在这个受限环境中选择特定位置的动作。实验结果表明，尽管数据集中添加了噪声和不确定性，我们的方法可以实现精确的关键点检测和动作分类。

噪声环境中机械臂动作识别机器学习模型的鲁棒性评估

Robustness Evaluation of Machine Learning Models for Robot Arm Action  Recognition in Noisy Environments

No significant work has been done to directly merge two partially overlapping
scenes using NeRF representations. Given pre-trained NeRF models of a 3D scene
with partial overlapping, this paper aligns them with a rigid transform, by
generalizing the traditional registration pipeline, that is, key point
detection and point set registration, to operate on 3D density fields. To
describe corner points as key points in 3D, we propose to use universal
pre-trained descriptor-generating neural networks that can be trained and
tested on different scenes. We perform experiments to demonstrate that the
descriptor networks can be conveniently trained using a contrastive learning
strategy. We demonstrate that our method, as a global approach, can effectively
register NeRF models, thus making possible future large-scale NeRF construction
by registering its smaller and overlapping NeRFs captured individually.

本文提出了一种使用预训练的 NeRF 模型对 3D 场景进行对齐的方法，该方法通过将传统的关键点检测和点集对齐的流程应用于 3D 密度场来实现。为了在 3D 中描述角点作为关键点，我们建议使用通用的预训练描述符生成神经网络。通过对比学习策略，可以方便地训练描述符网络。我们的方法作为全局方法可以有效地注册 NeRF 模型，从而使未来的大规模 NeRF 构建成为可能。