Autonomous robots operating in real-world environments encounter a variety of
objects that can be both rigid and articulated in nature. Having knowledge of
these specific object properties not only helps in designing appropriate
manipulation strategies but also aids in developing reliable tracking and pose
estimation techniques for many robotic and vision applications. In this
context, this paper presents a registration-based local region-to-region
mapping approach to classify an object as either articulated or rigid. Using
the point clouds of the intended object, the proposed method performs
classification by estimating unique local transformations between point clouds
over the observed sequence of movements of the object. The significant
advantage of the proposed method is that it is a constraint-free approach that
can classify any articulated object and is not limited to a specific type of
articulation. Additionally, it is a model-free approach with no learning
components, which means it can classify whether an object is articulated
without requiring any object models or labelled data. We analyze the
performance of the proposed method on two publicly available benchmark datasets
with a combination of articulated and rigid objects. It is observed that the
proposed method can classify articulated and rigid objects with good accuracy.

该论文提出了一种基于点云和本地转换的方法来对物体进行分类，以确定其是否是刚性或关节的。该方法具有无约束性和无模型的优势，可在不需要任何对象模型或已标记的数据的情况下识别物体的关节和刚性部分。作者在两个公开数据集上进行了性能测试，表明该方法能够以很高的准确度对物体进行关节和刚性的分类。

基于局部区域映射的方法对关节化物体进行分类

Local Region-to-Region Mapping-based Approach to Classify Articulated  Objects

Neural Networks sequentially build high-level features through their
successive layers. We propose here a new neural network model where each layer
is associated with a set of candidate mappings. When an input is processed, at
each layer, one mapping among these candidates is selected according to a
sequential decision process. The resulting model is structured according to a
DAG like architecture, so that a path from the root to a leaf node defines a
sequence of transformations. Instead of considering global transformations,
like in classical multilayer networks, this model allows us for learning a set
of local transformations. It is thus able to process data with different
characteristics through specific sequences of such local transformations,
increasing the expression power of this model w.r.t a classical multilayered
network. The learning algorithm is inspired from policy gradient techniques
coming from the reinforcement learning domain and is used here instead of the
classical back-propagation based gradient descent techniques. Experiments on
different datasets show the relevance of this approach.

本文提出了一种新的神经网络模型，其中每一层与一组候选映射相关联，通过顺序决策过程选择映射。该模型的结构类似于 DAG，能够学习一组本地变换，从而增强了该模型相对于经典的多层网络的表达能力。使用来自强化学习领域的策略梯度技术的学习算法，替代了传统的反向传播梯度下降技术。实验表明，这种方法具有很强的实用性。

深度顺序神经网络

Deep Sequential Neural Network

We present a simple characterization of equivalent Bayesian network
structures based on local transformations. The significance of the
characterization is twofold. First, we are able to easily prove several new
invariant properties of theoretical interest for equivalent structures. Second,
we use the characterization to derive an efficient algorithm that identifies
all of the compelled edges in a structure. Compelled edge identification is of
particular importance for learning Bayesian network structures from data
because these edges indicate causal relationships when certain assumptions
hold.

本文基于局部转换提出一种等价贝叶斯网络结构的简单特征描述，进一步证明了一些等价结构的理论性质并使用该描述推导出一种高效的算法，该算法能够识别贝叶斯网络结构中的所有强制边，强制边指示了在特定假设下的因果关系，对从数据中学习贝叶斯网络结构尤为重要。