  Simulation to real (Sim-to-Real) is an attractive approach to construct
controllers for robotic tasks that are easier to simulate than to analytically
solve. Working Sim-to-Real solutions have been demonstrated for tasks with a
clear single objective such as "reach the target". Real world applications,
however, often consist of multiple simultaneous objectives such as "reach the
target" but "avoid obstacles". A straightforward solution in the context of
reinforcement learning (RL) is to combine multiple objectives into a multi-term
reward function and train a single monolithic controller. Recently, a hybrid
solution based on pre-trained single objective controllers and a switching rule
between them was proposed. In this work, we compare these two approaches in the
multi-objective setting of a robot manipulator to reach a target while avoiding
an obstacle. Our findings show that the training of a hybrid controller is
easier and obtains a better success-failure trade-off than a monolithic
controller. The controllers trained in simulator were verified by a real
set-up.
