Jingwei Zhang
Albert-Ludwigs-Universität Freiburg |
About me
- 09/2014 ~ up to now: Working at the Autonomous Intelligent Systems group of Prof. Dr. Wolfram Burgard as a PhD student (leave of absence 02/2015 ~ 07/2015)
- 08/2011 ~ 12/2013: Master o.Sc. in Mechanical Engineering, Carnegie Mellon University
- 09/2007 ~ 06/2011: Bachelor o.Sc. in Measurement and Control Instruments and Technology with minor in Computer Science, Chengdu University of Technology, University of Electronic Science and Technology of China
Academic Websites
Internships
- 07/2020 ~ 12/2020: Research Scientist Intern, Noah's Ark Lab, Huawei
- 07/2019 ~ 09/2019: Research Scientist Intern, AI Research, Apple
- 03/2014 ~ 08/2014: Software Engineer Intern, Near Earth Autonomy
Research Interests
- Deep Reinforcement Learning
- Autonomous Navigation
Teaching
- TA, Deep Learning Lab Course, University of Freiburg, WS2017/18
- TA, Deep Learning Lab Course, University of Freiburg, WS2016/17
Publications
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Shengchao Yan, Jingwei Zhang, Daniel Buescher, Wolfram Burgard
Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning
To appear in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, 2020
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Jingwei Zhang, Niklas Wetzel, Nicolai Dorka, Joschka Boedecker, Wolfram Burgard
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
In Proceedings of the workshop of Exploration in Reinforcement Learning (Spotlight Talk), Internatioal Conference on Machine Learning (ICML), Long Beach, USA, 2019
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Jingwei Zhang, Lei Tai, Peng Yun, Yufeng Xiong, Ming Liu, Joschka Boedecker, Wolfram Burgard
VR Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
IEEE Robotics and Automation Letters (RA-L), 4(2):1148-1155, 2019
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Lei Tai, Jingwei Zhang, Ming Liu, Wolfram Burgard
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018
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Oleksii Zhelo, Jingwei Zhang, Lei Tai, Ming Liu, Wolfram Burgard
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
In Proceedings of the workshop of Machine Learning in Planning and Control of Robot Motion, IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018
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Wolfram Burgard, Abhinav Valada, Noha Radwan, Tayyab Naseer, Jingwei Zhang, Johan Vertens, Oier Mees, Andreas Eitel and Gabriel Oliveira
Perspectives on Deep Multimodel Robot Learning
In Proceedings of the International Symposium on Robotics Research (ISRR), Puerto Varas, Chile, 2017
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Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, Wolfram Burgard
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017
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Technical Reports
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Jingwei Zhang, Lei Tai, Ming Liu, Joschka Boedecker, Wolfram Burgard
Neural SLAM: Learning to Explore with External Memory
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Lei Tai, Jingwei Zhang, Ming Liu, Joschka Boedecker, Wolfram Burgard
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
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Supervised Thesis/Projects
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Baohe Zhang
Using Graph Neural Network as Dynamics Model
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Vigan Maxhera
Adjusting Velocities with Deep Reinforcement Learning
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Tobias Demmler
Deep Reinforcement Learning for Optimal Traffic Junction Behavior
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Yufeng Xiong
Targer-driven Visual Navigation for Mobile Robots throught Deep Reinforcement Learning
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Lukas Hermann
Vision-based Robot Manipulation using Natural Policy Gradient in Simulated Environments
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Oleksii Zhelo
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning