3 citations · 3 across the 2 of their papers we have counts for
10 papers
Crowd-Driven Mapping, Localization and Planning
Tingxiang Fan, Dawei Wang, Wenxi Liu +1
Navigation in dense crowds is a well-known open problem in robotics with many challenges in mapping, localization, and planning. Traditional solutions consider dense pedestrians as…
Autonomous Social Distancing in Urban Environments using a Quadruped Robot
Tingxiang Fan, Zhiming Chen, Xuan Zhao +5
COVID-19 pandemic has become a global challenge faced by people all over the world. Social distancing has been proved to be an effective practice to reduce the spread of COVID-19.…
Modeling 3D Shapes by Reinforcement Learning
Cheng Lin, Tingxiang Fan, Wenping Wang +1
We explore how to enable machines to model 3D shapes like human modelers using deep reinforcement learning (RL). In 3D modeling software like Maya, a modeler usually creates a mesh…
Learning Resilient Behaviors for Navigation Under Uncertainty
Tingxiang Fan, Pinxin Long, Wenxi Liu +3
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not…
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance
Qingyang Tan, Tingxiang Fan, Jia Pan +1
We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and global inform…
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…