  This paper investigates a hybrid solution which combines deep reinforcement
learning (RL) and classical trajectory planning for the following in front
application. Here, an autonomous robot aims to stay ahead of a person as the
person freely walks around. Following in front is a challenging problem as the
user's intended trajectory is unknown and needs to be estimated, explicitly or
implicitly, by the robot. In addition, the robot needs to find a feasible way
to safely navigate ahead of human trajectory. Our deep RL module implicitly
estimates human trajectory and produces short-term navigational goals to guide
the robot. These goals are used by a trajectory planner to smoothly navigate
the robot to the short-term goals, and eventually in front of the user. We
employ curriculum learning in the deep RL module to efficiently achieve a high
return. Our system outperforms the state-of-the-art in following ahead and is
more reliable compared to end-to-end alternatives in both the simulation and
real world experiments. In contrast to a pure deep RL approach, we demonstrate
zero-shot transfer of the trained policy from simulation to the real world.
