69 citations · 145 across the 15 of their papers we have counts for
5 papers · 1 filter
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Fan Wang, Bo Zhou, Ke Chen +5
Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety an…
Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds
Tingxiang Fan, Xinjing Cheng, Jia Pan +4
We aim to enable a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging envir…
Safe Navigation with Human Instructions in Complex Scenes
Zhe Hu, Jia Pan, Tingxiang Fan +2
In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons b…
Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios
Tingxiang Fan, Pinxin Long, Wenxi Liu +1
In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, o…
CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios
Tingxiang Fan, Xinjing Cheng, Jia Pan +2
Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training fram…