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.

中文翻译：

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接球：通过逆动力学学习为移动机械手提供准确的高速运动

移动机械手由配备一个或多个机械臂的移动平台组成，由于其扩展的工作空间和灵巧性，可用于执行各种具有挑战性的任务。通常，移动机械手部署在慢动作协作机器人场景中。在本文中，我们考虑需要精确高速运动的场景。我们为这种任务引入了一个框架，包括两个主要部分：（i）用于实时轨迹生成的双层运动优化算法，分别依赖于顺序二次规划（SQP）和二次规划（QP）；(ii) 基于学习的控制器，优化用于通过学习的逆动力学模型精确跟踪高速运动。我们通过大量高速接球实验使用移动机械手平台评估我们的框架，我们显示成功率为 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，并创造了新的技术水平。