AIRobers


Ph.D. Students


M.Sc. Students


Undergraduate Students


Alumni


Current Members


Dingcheng Hu (Ph.D. Student)
Dingcheng received a B.S. in Computer Science in 2018 and an M.S. in Computer Science in 2020, both from the University of California, San Diego. His main research interests include machine learning, robotics, and reinforcement learning.

Dingyi Sun (Ph.D. Student)
Dingyi received a B.Eng. in Electrical Engineering and Automation from Huazhong University of Science and Technology in 2018 and an M.S. in Electrical and Computer Engineering (Robotics Track) from the University of Michigan in 2020. He is interested in path planning, machine learning, and robotic perception.

Danoosh Chamani (M.Sc. Student)
Danoosh received a B.Sc. in Computer Software Engineering from the University of Tehran in 2019. He is interested in reinforcement learning, machine learning, and robotics.

Baiyu Li (M.Sc. Student)
Baiyu received a B.Eng. in Computer Science from the Northeastern University (Shenyang, China) in 2020. He is interested in path planning, multi-agent system, and parallel computing.

Qiushi Lin (M.Sc. Student)
Qiushi received a B.Eng. in Computer Science and Technology from Southern University of Science and Technology in 2020. He is interested in machine learning, reinforcement learning, and multi-agent system.

Qinghong Xu (M.Sc. Student)
Qinghong received a B.S. in Computational Mathematics from Xiamen University in 2017 and an M.S. in Computational and Applied Mathematics from Simon Fraser University in 2019. She is interested in multi-agent systems, path planning, and machine learning.

Xinyi Zhong (M.Sc. Student)
Xinyi received a B.C.S. Honours in Computer Science from Carleton University in 2019. She is interested in path planning, multi-agent system, and robotics.

Alumni


Ziyuan Ma (Former Undergraduate Student)
Ziyuan received a B.Sc. in Computing Science from Simon Fraser University in 2020. He was an undergraduate research student in our lab in 2020/2021.
  • Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent methods have applied reinforcement learning (RL) to learn decentralized polices in partially observable environments. A fundamental challenge of obtaining collision-free policy is that agents need to learn cooperation to handle congested situations. This paper combines communication with deep Q-learning to provide a novel learning based method for MAPF, where agents achieve cooperation via graph convolution. To guide RL algorithm on long-horizon goal-oriented tasks, we embed the potential choices of shortest paths from single source as heuristic guidance instead of using a specific path as in most existing works. Our method treats each agent independently and trains the model from a single agent’s perspective. The final trained policy is applied to each agent for decentralized execution. The whole system is distributed during training and is trained under a curriculum learning strategy. Empirical evaluation in obstacle-rich environment indicates the high success rate with low average step of our method.
    @inproceedings{MaICRA21,
     author = {Ziyuan Ma and Yudong Luo and Hang Ma},
     booktitle = {IEEE International Conference on Robotics and Automation},
     pages = {(in press)},
     title = {Distributed Heuristic Multi-Agent Path Finding with Communication},
     year = {2021}
    }


Yudong Luo (Former Visitor)
Yudong is a Ph.D. student at the University of Waterloo. He received a B.Eng. in Computer Science from Shanghai Jiao Tong University in 2018 and an M.Sc. in Computing Science from Simon Fraser University in 2020. He is interested in reinforcement learning, machine learning, and multi-agent system. Yudong visited our lab for 12 months in 2020/2021. More information can be found on his homepage.
  • Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent methods have applied reinforcement learning (RL) to learn decentralized polices in partially observable environments. A fundamental challenge of obtaining collision-free policy is that agents need to learn cooperation to handle congested situations. This paper combines communication with deep Q-learning to provide a novel learning based method for MAPF, where agents achieve cooperation via graph convolution. To guide RL algorithm on long-horizon goal-oriented tasks, we embed the potential choices of shortest paths from single source as heuristic guidance instead of using a specific path as in most existing works. Our method treats each agent independently and trains the model from a single agent’s perspective. The final trained policy is applied to each agent for decentralized execution. The whole system is distributed during training and is trained under a curriculum learning strategy. Empirical evaluation in obstacle-rich environment indicates the high success rate with low average step of our method.
    @inproceedings{MaICRA21,
     author = {Ziyuan Ma and Yudong Luo and Hang Ma},
     booktitle = {IEEE International Conference on Robotics and Automation},
     pages = {(in press)},
     title = {Distributed Heuristic Multi-Agent Path Finding with Communication},
     year = {2021}
    }