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 …

Achieving safe and effective navigation in crowds is a crucial yet challenging problem.
Recent work has mainly encoded the pedestrian-robot state pairs, which cannot fully capture …

Navigating in crowd environments is challenging for mobile robots because not only the
safety but also the comfort of surrounding pedestrians must be considered. In this work, a …

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 …

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 …

The growing development of autonomous systems is driving the application of mobile robots
in crowded environments. These scenarios often require robots to satisfy multiple conflicting …

Mobile robots operating in public environments require the ability to navigate among
humans and obstacles in a socially compliant and safe manner. Previous work has shown …

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 …

Robots navigating in a robot–human coexisting environment need to optimize their paths not
only for task-related performance (eg safety and efficiency) but also for their social …

Ensuring safe and efficient navigation in crowded environments is a critical goal for assistive
robots. Recent studies have emphasized the potential of deep reinforcement learning …