Navigating mobile robots along time-efficient and collision-free paths in crowds is still an
open and challenging problem. The key is to build a profound understanding of the crowd …

Safe and efficient navigation through human crowds is an essential capability for mobile
robots. Previous work on robot crowd navigation assumes that the dynamics of all agents …

It is challenging for a mobile robot to navigate through human crowds. Existing approaches
usually assume that pedestrians follow a predefined collision avoidance strategy, like social …

In the crowd navigation, reinforcement learning based on graph neural network is a
promising method, which effectively solves the poor navigation effect based on social …

Mobility in an effective and socially-compliant manner is an essential yet challenging task for
robots operating in crowded spaces. Recent works have shown the power of deep …

Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task.
Previous work has shown the power of deep reinforcement learning frameworks to train …

We study the problem of safe and intention-aware robot navigation in dense and interactive
crowds. Most previous reinforcement learning (RL) based methods fail to consider different …

We present a relational graph learning approach for robotic crowd navigation using model-
based deep reinforcement learning that plans actions by looking into the future. Our …

This work presents a deep reinforcement learning framework for interactive navigation in a
crowded place. Our proposed Learning to Balance (L2B) framework enables mobile robot …

Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a
very challenging task. This is because human movement and interactions are very hard to …