Robotics systems are becoming more and more autonomous and reconfigurable. In this context, the design of algorithms capable of deriving kinematics and dynamics models directly from data could be particularly useful. In this article, we present an algorithm that learns a forward kinematics model of a robot starting from a time series of visual observations. Our strategy can be applied to any robot with serial kinematics composed of revolute and prismatics joints. First, the algorithm identifies the robot kinematic structure, i.e., a high-level description of the robot geometry that defines the connections between the rigid-bodies composing the robot. Then, the algorithm derives the forward kinematics relying on a Gaussian process (GP) model. More precisely, the GP model is based on a polynomial kernel, defined exploiting the kinematic structure previously identified. The effectiveness of the proposed solution has been tested via extensive Monte Carlo simulations, as well as via experiments on a real UR10 robot.

中文翻译：

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机器人运动模型的自主学习

机器人系统正变得越来越自主和可重新配置。在这种情况下，能够直接从数据推导出运动学和动力学模型的算法设计可能特别有用。在本文中，我们提出了一种算法，该算法从视觉观察的时间序列开始学习机器人的正向运动学模型。我们的策略可以应用于任何具有由旋转关节和棱柱关节组成的串行运动学的机器人。首先，该算法识别机器人运动学结构，即定义构成机器人的刚体之间的连接的机器人几何结构的高级描述。然后，该算法根据高斯过程 (GP) 模型推导出正向运动学。更准确地说，GP 模型基于多项式内核，定义利用先前确定的运动学结构。所提出的解决方案的有效性已经通过广泛的蒙特卡罗模拟以及真实 UR10 机器人的实验进行了测试。