错误机器人模型下CRiSP逆运动学学习的结构化预测
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
摘要：
随着机器学习的最新进展，传统上需要精确建模才能分析解决的问题现在可以通过数据驱动策略成功解决。其中，计算冗余机器人手臂的逆运动学提出了一个重大的挑战，由于机器人的非线性结构，硬关节约束和不可逆的运动学地图。此外，大多数学习算法考虑一个完全的数据驱动的方法，而往往是有用的信息的结构的机器人是可用的，应该积极利用。在这项工作中，我们提出了一个简单的，但有效的，学习的逆运动学的方法。我们引入了一种结构化预测算法，该算法将数据驱动策略与正运动学函数提供的模型相结合-即使该函数被错误指定-以准确地解决问题。所提出的方法，确保预测的关节配置以及机器人的约束。我们还提供了统计保证我们的估计的泛化性能，以及经验评估其性能的轨迹重建任务。
英文摘要：
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 misspecified -- 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.
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