  We present a generalised architecture for reactive mobile manipulation while
a robot's base is in motion toward the next objective in a high-level task. By
performing tasks on-the-move, overall cycle time is reduced compared to methods
where the base pauses during manipulation. Reactive control of the manipulator
enables grasping objects with unpredictable motion while improving robustness
against perception errors, environmental disturbances, and inaccurate robot
control compared to open-loop, trajectory-based planning approaches. We present
an example implementation of the architecture and investigate the performance
on a series of pick and place tasks with both static and dynamic objects and
compare the performance to baseline methods. Our method demonstrated a
real-world success rate of over 99%, failing in only a single trial from 120
attempts with a physical robot system. The architecture is further demonstrated
on other mobile manipulator platforms in simulation. Our approach reduces task
time by up to 48%, while also improving reliability, gracefulness, and
predictability compared to existing architectures for mobile manipulation. See
https://benburgesslimerick.github.io/ManipulationOnTheMove for supplementary
materials.
