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Jianyu Chen

AQZ PI(July 2021 to present)
THU Assistant Professor

Biography

Shanghai Qi Zhi Institute PI, Assistant Professor at IIIS, Tsinghua.

Jianyu Chen obtained his bachelor's degree from Tsinghua University and his doctor's degree from the University of California, Berkeley, under the supervision of Professor Masayoshi Tomizuka. He is working in the cross fields of robotics, reinforcement learning, control and autonomous driving. His research goal is to build advanced robotic systems with high performance and high intelligence. He has published more than 40 papers in flagship international conferences and journals in the fields of robotics and artificial intelligence, and some of the papers have been selected as best paper finalists for L4DC 2022, IEEE IV 2021 and IFAC MECC 2021.


Personal honor: 

2021 Forbes China 30 under 30 (Science List)

Research Direction

Humanoid  Robot

Building the hardware and motion control systems for humanoid general intelligent robot.

Embodied AGI

Building a general AI agent with hands and legs, capable of listening and speaking, and capable of being loaded on an entity robot to interact with the real physical world.

Members

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Open positions

Research Direction:

Artificial Intelligence and Robotics: Reinforcement Learning, Robotics, Machine Learning, Computer Vision, and other related areas.

Responsibilities:

1. Responsible for theoretical research, algorithm or system development in the above-mentioned related fields and directions.

2. Publish academic achievements or innovative research results in the above-mentioned related fields.

Required Qualification:

1. Have a background in Computer Science, Electronics, Automation, Software, Physics or other related fields, and strong academic capabilities.

2. Possess excellent foundational knowledge and programming skills (Python, Linux, C++, etc.) in the relevant field.

3. Have actual work experience or project participation experience in related research fields.

4. Able to conduct independent research work and have high academic goals and pursuits.

5. Priority will be given to those who have published influential academic works at home and abroad.

Please send your CV:

chernjianyu@sqz.ac.cn


News

We are actively recruiting Postdocs, Engineers, PhDs, Masters and RAs, please drop me an email with your resume via jianyuchen@tsinghua.edu.cn.

2022-12: 1 papers accpeted to RAL!

2022-11: 1 papers accpeted to RAL and one paper accepted to TNNLS!

2022-09: 3 papers accpeted to NeurIPS 2022 and one paper accepted to CoRL 2022!

2022-07: One paper received best paper finalist in L4DC 2022 and one paper accpeted to CDC 2022!

2022-06: One paper accepted to IROS 2022!

2022-05: 3 papers accepted to ICML 2022!

2022-04: One paper accepted to TNNLS!

2022-03: 2 papers accepted to L4DC 2022!

2022-01: One paper accepted to ICRA 2022!


Paper/Publication

16. Yanjiang Guo, Zheyuan Jiang, Yen-Jen Wang, Jingyue Gao, Jianyu Chen, Decentralized Motor Skill Learning for Complex Robotic Systems, IEEE Robotics and Automation Letters (RA-L), 2023 查看PDF


15. Yujie Yang, Yuxuan Jiang, Jianyu Chen, Shengbo Eben Li, Ziqing Gu, Yuming Yin, Qian Zhang, Kai Yu, Belief State Actor-Critic Algorithm from Separation Principle for POMDP, American Control Conference (ACC), 2023 查看PDF


14. Yujie Yang, Yuxuan Jiang, Yichen Liu, Jianyu Chen, Shengbo Eben Li, Model-Free Safe Reinforcement Learning Through, IEEE Robotics and Automation Letters (RA-L), 2023 查看PDF 


13. Hai Zhong, Yutaka Shimizu, Jianyu Chen, Chance-Constrained Iterative Linear-Quadratic Stochastic Games, IEEE Robotics and Automation Letters (RA-L), 2022 查看PDF


12. Yanjiang Guo, Jingyue Gao, Zheng Wu, Chengming Shi, Jianyu Chen, Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward, International Conference on Robots Learning (CORL), 2022 查看PDF


11. Zheyuan Jiang, Jingyue Gao, Jianyu Chen, Unsupervised Skill Discovery via Recurrent Skill Training, Conference on Neural Information Processing Systems (NeurIPS), 2022 查看PDF


10. Xiang Zhu, Shucheng Kang, Jianyu Chen, A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation, International Conference on Intelligent Robots and Systems (IROS), 2022 查看PDF


9. Xiaoyu Chen, Yao Mark Mu, Ping Luo, Shengbo Eben Li, Jianyu Chen, Flow-based Recurrent Belief State Learning for POMDPs, International Conference on Machine Learning  (ICML), 2022 查看PDF


8. Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu Chen, Reachability Constrained Reinforcement Learning, International Conference on Machine Learning (ICML), 2022 查看PDF


7Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen, Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning, Conference on Learning for Dynamics and Control (L4DC), 2022 查看PDF


6. Yuheng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng, Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning, IEEE Conference on Decision and Control (CDC), 2022 查看PDF


5. Yujie Yang, Jianyu Chen, Shengbo Li, Learning POMDP Models with Similarity Space Regularization: a Linear Gaussian Case Study, Conference on Learning for Dynamics and Control (L4DC), 2022 查看PDF


4. Baiyu Peng, Jingliang Duan, Jianyu Chen, Shengbo Eben Li, Genjin Xie, Congsheng Zhang, Yang Guan, Yao Mu, Enxin Sun, Model-Based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022 查看PDF


3. Haitong Ma, Jianyu Chen,Shengbo Eben, Ziyu Lin,Yang Guan, Yangang Ren, Sifa Zheng, Model-based Constrained Reinforcement Learning using Generalized Control Barrier FunctionInternational Conference on Intelligent Robots and Systems (IROS), 2021 查看PDF


2. Jianyu Chen, Yutaka Shimizu, Liting Sun, Masayoshi Tomizuka, Wei Zhan, Constrained Iterative LQG for Real-Time Chance-Constrained Gaussian Belief Space Planning, International Conference on Intelligent Robots and Systems (IROS), 2021 查看PDF


1. Jianyu Chen, Shengbo Eben Li, Masayoshi Tomizuka, Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning, IEEE Transactions on Intelligent Transportation Systems (TITS), 2021 查看PDF