With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function -- even when this function is misspeficied -- to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks.

中文翻译：

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使用错误指定的机器人模型进行CRiSP逆运动学学习的结构化预测

随着机器学习的最新进展，传统上需要解析精确地建模才能解决的问题现在可以通过数据驱动策略成功解决。其中，由于机器人的非线性结构，硬关节约束和不可逆运动学图，计算冗余机器人手臂的逆运动学提出了重大挑战。此外，大多数学习算法都考虑采用完全由数据驱动的方法，而有关机器人结构的有用信息通常是可用的，应予以积极利用。在这项工作中，我们提出了一种简单但有效的方法来学习逆运动学。我们引入了一种结构化的预测算法，该算法将数据驱动策略与正向运动学功能提供的模型相结合-即使该功能使用不当，也可以准确地解决问题。所提出的方法可确保预测的关节配置完全在机器人的约束范围内。我们还为估计器的泛化属性提供统计保证，并对其在轨迹重建任务中的性能进行实证评估。