N$^2$M$^2$：在未知和动态环境中学习任意移动操作运动导航
N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments
AI总结：
本文提出 N$^2$M$^2$ 方法，通过将移动操作任务分解为末端执行器运动生成器与移动底盘强化学习智能体，使机器人在未知动态环境中实现长视野任务并即时避障。
摘要：
尽管在工业和服务机器人中都很重要，移动操作仍然是一个重大挑战，因为它需要将末端执行器轨迹生成与导航技能无缝集成，以及对长视野的推理。现有方法难以控制庞大的配置空间，也难以在动态和未知环境中导航。在之前的工作中，我们提出将移动操作任务分解为任务空间中末端执行器的简化运动生成器，以及用于移动底盘的强化学习智能体，以保证运动的运动学可行性。在本研究中，我们引入了 Neural Navigation for Mobile Manipulation（N$^2$M$^2$），将这种分解扩展到复杂障碍物环境，使其能够在真实世界环境中处理广泛的任务。由此产生的方法可以在未探索环境中执行未知的、长视野任务，同时对动态障碍物和环境变化做出即时反应。同时，它提供了一种简单的方法来定义新的移动操作任务。我们在广泛的仿真和真实世界实验中，使用多种运动学特性各异的移动操作机器人展示了所提方法的能力。代码和视频公开于 http://mobile-rl.cs.uni-freiburg.de。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
