  Mobile manipulation tasks remain one of the critical challenges for the
widespread adoption of autonomous robots in both service and industrial
scenarios. While planning approaches are good at generating feasible whole-body
robot trajectories, they struggle with dynamic environments as well as the
incorporation of constraints given by the task and the environment. On the
other hand, dynamic motion models in the action space struggle with generating
kinematically feasible trajectories for mobile manipulation actions. We propose
a deep reinforcement learning approach to learn feasible dynamic motions for a
mobile base while the end-effector follows a trajectory in task space generated
by an arbitrary system to fulfill the task at hand. This modular formulation
has several benefits: it enables us to readily transform a broad range of
end-effector motions into mobile applications, it allows us to use the
kinematic feasibility of the end-effector trajectory as a dense reward signal
and its modular formulation allows it to generalise to unseen end-effector
motions at test time. We demonstrate the capabilities of our approach on
multiple mobile robot platforms with different kinematic abilities and
different types of wheeled platforms in extensive simulated as well as
real-world experiments.
