  Efficient use of the space in an elevator is very necessary for a service
robot, due to the need for reducing the amount of time caused by waiting for
the next elevator. To provide a solution for this, we propose a hybrid approach
that combines reinforcement learning (RL) with voice interaction for robot
navigation in the scene of entering the elevator. RL provides robots with a
high exploration ability to find a new clear path to enter the elevator
compared to traditional navigation methods such as Optimal Reciprocal Collision
Avoidance (ORCA). The proposed method allows the robot to take an active clear
path action towards the elevator whilst a crowd of people stands at the
entrance of the elevator wherein there are still lots of space. This is done by
embedding a clear path action (voice prompt) into the RL framework, and the
proposed navigation policy helps the robot to finish tasks efficiently and
safely. Our model approach provides a great improvement in the success rate and
reward of entering the elevator compared to state-of-the-art navigation
policies without active clear path operation.
