Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

As mobile robots are increasingly introduced into our daily lives, it grows ever more
imperative that these robots navigate with and among people in a safe and socially …

Observing social/physical distancing norms between humans has become an indispensable
precaution to slow down the transmission of COVID-19. We present a novel method to …

The challenges to solving the collision avoidance problem lie in adaptively choosing optimal
robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a …

Motion planning is critical to realize the autonomous operation of mobile robots. As the
complexity and randomness of robot application scenarios increase, the planning capability …

We present a novel Deep Reinforcement Learning (DRL) based policy to compute
dynamically feasible and spatially aware velocities for a robot navigating among mobile …

We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that
arises when a robot navigates through dense scenarios and crowds. Our method senses …

Humans are well-adept at navigating public spaces shared with others, where current
autonomous mobile robots still struggle: while safely and efficiently reaching their goals …

While current autonomous navigation systems allow robots to successfully drive themselves
from one point to another in specific environments, they typically require extensive manual …