In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data. Often, this data comprises variables that inherently carry geometric constraints, such as the unit-norm condition of quaternions representing rigid-body orientations or the positive definiteness of stiffness and manipulability ellipsoids. Handling such geometric constraints effectively requires the incorporation of tools from differential geometry into the formulation of machine learning methods. In this context, Riemannian manifolds emerge as a powerful mathematical framework to handle such geometric constraints. Nevertheless, their recent adoption in robot learning has been largely characterized by a mathematically-flawed simplification, hereinafter referred to as the ``single tangent space fallacy". This approach involves merely projecting the data of interest onto a single tangent (Euclidean) space, over which an off-the-shelf learning algorithm is applied. This paper provides a theoretical elucidation of various misconceptions surrounding this approach and offers experimental evidence of its shortcomings. Finally, it presents valuable insights to promote best practices when employing Riemannian geometry within robot learning applications.

翻译：在机器人学领域，众多下游机器人任务利用机器学习方法来处理、建模或综合数据。此类数据通常包含天生具有几何约束的变量，例如表示刚体姿态的四元数的单位范数条件，或刚度和可操作椭球的正定性。有效处理这些几何约束需要将微分几何工具融入机器学习方法的构建中。在此背景下，黎曼流形成为处理此类几何约束的强大数学框架。然而，近期其在机器人学习中的应用大多基于一种数学上有缺陷的简化方法，下文称为"单一切空间谬误"。该方法仅将感兴趣的数据投影到单个切（欧几里得）空间上，然后在该空间上应用现成的学习算法。本文从理论上阐明了围绕该方法的若干误解，并提供了其缺陷的实验证据。最后，本文提出了宝贵见解，以推广在机器人学习应用中正确使用黎曼几何的最佳实践。