  Robot assistants are emerging as high-tech solutions to support people in
everyday life. Following and assisting the user in the domestic environment
requires flexible mobility to safely move in cluttered spaces. We introduce a
new approach to person following for assistance and monitoring. Our methodology
exploits an omnidirectional robotic platform to detach the computation of
linear and angular velocities and navigate within the domestic environment
without losing track of the assisted person. While linear velocities are
managed by a conventional Dynamic Window Approach (DWA) local planner, we
trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular
velocities commands and maintain the orientation of the robot towards the user.
We evaluate our navigation system on a real omnidirectional platform in various
indoor scenarios, demonstrating the competitive advantage of our solution
compared to a standard differential steering following.
