Reinforcement Learning Approach to Clear Paths of Robots in Elevator Environment

Efficiently using the space of an elevator for a service robot is very necessary, due to the need for reducing the amount of time caused by waiting for the next elevator. To solve 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 the 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 (beep) into the RL framework, and the proposed navigation policy leads 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 ORCA and RL navigation policy without beep.