Reinforcement Learning based Voice Interaction to Clear Path for Robots in Elevator Environment

TL;DR

This paper introduces a hybrid reinforcement learning and voice interaction approach to improve robot navigation efficiency and safety when entering elevators in crowded environments.

Contribution

It proposes a novel integration of voice prompts into reinforcement learning for active path clearing, enhancing robot navigation in elevator scenarios.

Findings

Significant improvement in success rate of elevator entry.

Higher reward scores compared to traditional methods.

Enhanced safety and efficiency in crowded environments.

Abstract

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.