We present a comprehensive framework for studying and leveraging
morphological symmetries in robotic systems. These are intrinsic properties of
the robot's morphology, frequently observed in animal biology and robotics,
which stem from the replication of kinematic structures and the symmetrical
distribution of mass. We illustrate how these symmetries extend to the robot's
state space and both proprioceptive and exteroceptive sensor measurements,
resulting in the equivariance of the robot's equations of motion and optimal
control policies. Thus, we recognize morphological symmetries as a relevant and
previously unexplored physics-informed geometric prior, with significant
implications for both data-driven and analytical methods used in modeling,
control, estimation and design in robotics. For data-driven methods, we
demonstrate that morphological symmetries can enhance the sample efficiency and
generalization of machine learning models through data augmentation, or by
applying equivariant/invariant constraints on the model's architecture. In the
context of analytical methods, we employ abstract harmonic analysis to
decompose the robot's dynamics into a superposition of lower-dimensional,
independent dynamics. We substantiate our claims with both synthetic and
real-world experiments conducted on bipedal and quadrupedal robots. Lastly, we
introduce the repository MorphoSymm to facilitate the practical use of the
theory and applications outlined in this work.

研究和利用机器人系统中的形态对称性，证明其对机器学习模型的样本效率和泛化能力有显著影响，并将机器人动力学分解为低维度、独立动力学，为机器人学中的建模、控制、估计和设计提供了新的物理信息几何先验。

机器人中的形态对称性

Morphological Symmetries in Robotics

Cardiac diseases are one of the leading mortality factors in modern,
industrialized societies, which cause high expenses in public health systems.
Due to high costs, developing analytical methods to improve cardiac diagnostics
is essential. The heart's electric activity was first modeled using a set of
nonlinear differential equations. Following this, variations of cardiac spectra
originating from deterministic dynamics are investigated. Analyzing a normal
human heart's power spectra offers His-Purkinje network, which possesses a
fractal-like structure. Phase space trajectories are extracted from the time
series electrocardiogram (ECG) graph with third-order derivate Taylor Series.
Here in this study, phase space analysis and Convolutional Neural Networks
(CNNs) method are applied to 44 records via the MIT-BIH database recorded with
MLII. In order to increase accuracy, a straight line is drawn between the
highest Q-R distance in the phase space images of the records. Binary CNN
classification is used to determine healthy or unhealthy hearts. With a 90.90%
accuracy rate, this model could classify records according to their heart
status.

本研究旨在应用相空间分析和卷积神经网络方法对来自 MIT-BIH 数据库的 44 个记录进行分类识别，以提高对心脏健康状况的诊断精度，结果表明该方法可以达到 90.90% 的准确率。