  Navigation has been classically solved in robotics through the combination of
SLAM and planning. More recently, beyond waypoint planning, problems involving
significant components of (visual) high-level reasoning have been explored in
simulated environments, mostly addressed with large-scale machine learning, in
particular RL, offline-RL or imitation learning. These methods require the
agent to learn various skills like local planning, mapping objects and querying
the learned spatial representations. In contrast to simpler tasks like waypoint
planning (PointGoal), for these more complex tasks the current state-of-the-art
models have been thoroughly evaluated in simulation but, to our best knowledge,
not yet in real environments.
  In this work we focus on sim2real transfer. We target the challenging
Multi-Object Navigation (Multi-ON) task and port it to a physical environment
containing real replicas of the originally virtual Multi-ON objects. We
introduce a hybrid navigation method, which decomposes the problem into two
different skills: (1) waypoint navigation is addressed with classical SLAM
combined with a symbolic planner, whereas (2) exploration, semantic mapping and
goal retrieval are dealt with deep neural networks trained with a combination
of supervised learning and RL. We show the advantages of this approach compared
to end-to-end methods both in simulation and a real environment and outperform
the SOTA for this task.
