Autonomous vehicle (AV) is expected to reshape the future transportation system, and its
decision-making is one of the most critical modules. Many current decision-making modules …

This article proposes a novel learning-based control policy with strong generalizability to
new environments that enables a mobile robot to navigate autonomously through spaces …

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 focus on robot navigation in crowded environments. To navigate safely and efficiently
within crowds, robots need models for crowd motion prediction. Building such models is …

Robot navigation in crowded environments has recently benefited from advances in deep
reinforcement learning (DRL) approaches. However, it still presents a challenge to …

Mobile robot autonomous navigation in large-scale environments with crowded dynamic
objects and static obstacles is still an essential yet challenging task. Recent works have …

Real-world robot applications usually require navigating agents to face multiple
destinations. Besides, the real-world crowded environments usually contain dynamic and …

Deep reinforcement learning (DRL) is an essential technique for autonomous motion
planning of mobile robots in dynamic and uncertain environments. In attempting to acquire a …

In public spaces shared with humans, ensuring multi-robot systems navigate without
collisions while respecting social norms is challenging, particularly with limited …

We focus on robot navigation in crowded environments. The challenge of predicting the
motion of a crowd around a robot makes it hard to ensure human safety and comfort. Recent …