论文标题
接球：通过逆动力学学习，移动操纵器的准确高速运动
Catch the Ball: Accurate High-Speed Motions for Mobile Manipulators via Inverse Dynamics Learning
论文作者
论文摘要
移动操纵器由配备一个或多个机器人臂的移动平台组成，并且由于其扩展的工作区和灵巧性而引起了一系列具有挑战性的任务。通常，移动操纵器部署在慢动作协作机器人方案中。在本文中，我们考虑需要准确的高速运动的方案。我们引入了该任务制度的框架，包括两个主要组成部分：（i）实时轨迹生成的双层运动优化算法，该算法分别依赖于顺序的二次编程（SQP）和二次编程（QP）； （ii）通过学习的逆动力学模型优化了基于学习的控制器，用于精确跟踪高速运动。我们通过许多高速捕捉实验通过移动操纵器平台评估了我们的框架，在该实验中，我们的成功率为85.33％。据我们所知，这种成功率超过了现有相关系统的报告性能，并设定了新的最新状态。
Mobile manipulators consist of a mobile platform equipped with one or more robot arms and are of interest for a wide array of challenging tasks because of their extended workspace and dexterity. Typically, mobile manipulators are deployed in slow-motion collaborative robot scenarios. In this paper, we consider scenarios where accurate high-speed motions are required. We introduce a framework for this regime of tasks including two main components: (i) a bi-level motion optimization algorithm for real-time trajectory generation, which relies on Sequential Quadratic Programming (SQP) and Quadratic Programming (QP), respectively; and (ii) a learning-based controller optimized for precise tracking of high-speed motions via a learned inverse dynamics model. We evaluate our framework with a mobile manipulator platform through numerous high-speed ball catching experiments, where we show a success rate of 85.33%. To the best of our knowledge, this success rate exceeds the reported performance of existing related systems and sets a new state of the art.
