A comparative evaluation of machine learning methods for robot navigation through human crowds

TL;DR

This paper compares traditional pathfinding and prediction methods with reinforcement learning for robot navigation in crowds, demonstrating reinforcement learning's superior performance in safety and efficiency.

Contribution

It provides the first comprehensive comparative evaluation of these approaches on real-world crowd data, highlighting the advantages of reinforcement learning.

Findings

Reinforcement learning outperforms pathfinding and prediction methods.

Reinforcement learning achieves faster and safer navigation.

State-of-the-art reinforcement learning methods are most effective.

Abstract

Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety. Most approaches to date were focused on the combination of pathfinding algorithms with machine learning for pedestrian walking prediction. More recently, reinforcement learning techniques have been proposed in the research literature. In this paper, we perform a comparative evaluation of pathfinding/prediction and reinforcement learning approaches on a crowd movement dataset collected from surveillance videos taken at Grand Central Station in New York. The results demonstrate the strong superiority of state-of-the-art reinforcement learning approaches over pathfinding with state-of-the-art behaviour prediction techniques.