N$^{2}$M$^{2}$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments

Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires 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 the 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 the kinematic feasibility of the motion. In this work, we introduce Neural Navigation for Mobile Manipulation (N
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M
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), which extends this decomposition to complex obstacle environments, extends the agent's control to the torso joint and the norm of the end-effector motion velocities, uses a more general reward function and, thereby, enables robots to tackle a much broader 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.