  Many robotic path planning problems are continuous, stochastic, and
high-dimensional. The ability of a mobile manipulator to coordinate its base
and manipulator in order to control its whole-body online is particularly
challenging when self and environment collision avoidance is required.
Reinforcement Learning techniques have the potential to solve such problems
through their ability to generalise over environments. We study joint penalties
and joint limits of a state-of-the-art mobile manipulator whole-body controller
that uses LIDAR sensing for obstacle collision avoidance. We propose directions
to improve the reinforcement learning method. Our agent achieves significantly
higher success rates than the baseline in a goal-reaching environment and it
can solve environments that require coordinated whole-body control which the
baseline fails.
