Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires a seamless integration of end-effector trajectory generation with navigation skills as well as reasoning over long-horizons. Existing methods struggle to control the large configuration space, and to navigate dynamic and unknown environments. In previous work, we proposed to decompose mobile manipulation tasks into a simplified motion generator for the end-effector in task space and a trained reinforcement learning agent for the mobile base to account for kinematic feasibility of the motion. In this work, we introduce Neural Navigation for Mobile Manipulation (N$^2$M$^2$) which extends this decomposition to complex obstacle environments and enables it to tackle a broad range of tasks in real world settings. The resulting approach can perform unseen, long-horizon tasks in unexplored environments while instantly reacting to dynamic obstacles and environmental changes. At the same time, it provides a simple way to define new mobile manipulation tasks. We demonstrate the capabilities of our proposed approach in extensive simulation and real-world experiments on multiple kinematically diverse mobile manipulators. Code and videos are publicly available at http://mobile-rl.cs.uni-freiburg.de.
翻译:尽管机动操纵在工业和服务业机器人中都很重要,但它仍然是一项重大挑战,因为它需要将最终效应轨迹生成与导航技能以及长方位推理无缝地结合起来。现有的方法难以控制大型配置空间,难以驾驭动态和未知环境。在以往的工作中,我们提议将移动操纵任务分解成一个简化的动作生成器,用于任务空间中的终端效应者,并为移动基地提供一个经过培训的强化学习剂,以说明运动的动态可行性。在这项工作中,我们引入了移动操纵的神经导航($$2$M$2美元),将这种分解扩展至复杂的障碍环境,使其能够在现实世界环境中应对一系列广泛的任务。由此产生的方法可以在未探索的环境中执行不可见的长方位操纵任务,同时对动态障碍和环境变化作出即时反应。同时,它提供了界定新的移动操纵任务的简单方法。我们展示了在广泛模拟和现实世界对多种离心的移动操纵器进行实验中的拟议方法的能力。代码和视频可在 http://移动-rrefreburg.c.c.code and videls.