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 …

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 …

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 …

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 …

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 present DenseCAvoid, a novel algorithm for navigating a robot through dense crowds
and avoiding collisions by anticipating pedestrian behaviors. Our formulation uses visual …

Mobile robots capable of navigating seamlessly and safely in pedestrian rich environments
promise to bring robotic assistance closer to our daily lives. In this paper we draw on insights …

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths with limited observation …

Navigation is an essential capability for mobile robots. In this paper, we propose a
generalized yet effective 3M (ie, multi-robot, multi-scenario, and multi-stage) training …