论文标题
从避免障碍物到动态系统的本地旋转到运动学习
From Obstacle Avoidance To Motion Learning Using Local Rotation of Dynamical Systems
论文作者
论文摘要
在机器人技术中通常是从外部角度描述的，即，我们以数学方式提供有关特定（通常惯性）参考框架的障碍运动的信息。在当前的工作中，我们建议描述有关机器人本身的机器人运动。类似于我们彼此提供指令的方式（直奔多米之后，然后向左移动后，然后向右转动。），我们将指令作为相对旋转提供给机器人。我们首先引入一个避免障碍框架，该框架允许在试图保持接近初始（线性或非线性）动态系统的同时避免星形障碍物。本地旋转的框架扩展到运动学习。自动聚类定义了局部稳定性的区域，为此，可以单独学习精确的动力学。该框架已应用于LASA手写数据集，并显示出令人鼓舞的结果。
In robotics motion is often described from an external perspective, i.e., we give information on the obstacle motion in a mathematical manner with respect to a specific (often inertial) reference frame. In the current work, we propose to describe the robotic motion with respect to the robot itself. Similar to how we give instructions to each other (go straight, and then after multiple meters move left, and then a sharp turn right.), we give the instructions to a robot as a relative rotation. We first introduce an obstacle avoidance framework that allows avoiding star-shaped obstacles while trying to stay close to an initial (linear or nonlinear) dynamical system. The framework of the local rotation is extended to motion learning. Automated clustering defines regions of local stability, for which the precise dynamics are individually learned. The framework has been applied to the LASA-handwriting dataset and shows promising results.
