  Robotic navigation in environments shared with other robots or humans remains
challenging because the intentions of the surrounding agents are not directly
observable and the environment conditions are continuously changing. Local
trajectory optimization methods, such as model predictive control (MPC), can
deal with those changes but require global guidance, which is not trivial to
obtain in crowded scenarios. This paper proposes to learn, via deep
Reinforcement Learning (RL), an interaction-aware policy that provides
long-term guidance to the local planner. In particular, in simulations with
cooperative and non-cooperative agents, we train a deep network to recommend a
subgoal for the MPC planner. The recommended subgoal is expected to help the
robot in making progress towards its goal and accounts for the expected
interaction with other agents. Based on the recommended subgoal, the MPC
planner then optimizes the inputs for the robot satisfying its kinodynamic and
collision avoidance constraints. Our approach is shown to substantially improve
the navigation performance in terms of number of collisions as compared to
prior MPC frameworks, and in terms of both travel time and number of collisions
compared to deep RL methods in cooperative, competitive and mixed multiagent
scenarios.
