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Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles


May 02, 2021
Sharad Chitlangia, Zuxin Liu, Akhil Agnihotri, Ding Zhao


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Context-Aware Safe Reinforcement Learning for Non-Stationary Environments


Jan 02, 2021
Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Ding Zhao


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Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method


Oct 15, 2020
Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao

* 9 pages, 6 figures 

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MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments


Jul 30, 2020
Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao

* 6 pages, accepted at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) 

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Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes


Jun 29, 2020
Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao

* 16 pages, 6 figures 

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Delay-Aware Multi-Agent Reinforcement Learning


May 11, 2020
Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao


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Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach


Apr 07, 2019
Zuxin Liu, Mansur Arief, Ding Zhao

* 7 pages including the references, accepted by International Conference on Robotics and Automation (ICRA), 2019 

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DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments


Dec 05, 2018
Chao Yu, Zuxin Liu, Xinjun Liu, Fugui Xie, Yi Yang, Qi Wei, Qiao Fei

* 7 pages, accepted at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAM 

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