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：尽管移动操控在工业和服务机器人领域至关重要，但因其需要将末端执行器轨迹生成与导航技能以及长时域推理无缝整合，仍是一项重大挑战。现有方法难以控制庞大的构型空间，也难以在动态未知环境中实现导航。在前期工作中，我们提出将移动操控任务分解为：任务空间中末端执行器的简化运动生成器，以及用于运动学可行性分析的移动基座强化学习智能体。本研究提出面向移动操控的神经导航方法（N$^2$M$^2$），将上述分解扩展至复杂障碍环境，使其能够在真实场景中完成广泛任务。该方法可在未探索环境中执行未见过的长时域任务，同时即时响应动态障碍与环境变化，并提供简单定义新移动操控任务的方式。我们通过多组运动学异构移动操控平台的大量仿真与真实世界实验验证了该方法的能力。相关代码与视频已公开于 http://mobile-rl.cs.uni-freiburg.de。