论文标题
n $^2 $ m $^2 $：在看不见和动态环境中的任意移动操作动作的学习导航
N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments
论文作者
论文摘要
尽管移动操作在工业和服务机器人方面都很重要，但移动操作仍然是一个重大挑战，因为它需要无缝整合最终效应轨迹与导航技能以及对长匹马的推理。现有方法难以控制大型配置空间，并导航动态和未知环境。在以前的工作中，我们建议将移动操纵任务分解为任务空间中最终效果的简化运动生成器，以及训练有素的增强式学习代理，以说明运动的运动可行性。在这项工作中，我们引入了移动操作的神经导航（n $^2 $ m $^2 $），该导航将这种分解扩展到复杂的障碍环境，并使其能够解决现实世界中的广泛任务。最终的方法可以在未探索的环境中执行看不见的长马任务，同时立即对动态障碍和环境变化做出反应。同时，它提供了一种定义新的移动操作任务的简单方法。我们证明了我们提出的方法在多个运动学上多样化的移动操纵器上进行了广泛的模拟和现实实验的能力。代码和视频可在http://mobile-rl.cs.uni-freiburg.de上公开获得。
Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires a seamless integration of end-effector trajectory generation with navigation skills as well as reasoning over long-horizons. Existing methods struggle to control the large configuration space, and to navigate dynamic and unknown environments. In previous work, we proposed to decompose mobile manipulation tasks into a simplified motion generator for the end-effector in task space and a trained reinforcement learning agent for the mobile base to account for kinematic feasibility of the motion. In this work, we introduce Neural Navigation for Mobile Manipulation (N$^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.
