Chengshu (Eric) Li
I am currently a first-year Ph.D. student in Computer Science at Stanford University. I am advised by Prof. Fei-Fei Li and Prof. Silvio Savarese at the Stanford Vision and Learning Lab. My research interest lies at the intersection of robot learning and computer vision.
I received my B.S. in Computer Science with Distinction from Stanford University in 2017 and M.S. in Management Science & Engineering from Stanford University in 2020. In the past, I've worked/interned at Google Brain Robotics (2019-2020), AutoX (2017-2018), Shift (2016), and Tableau (2015).
Email: chengshu@stanford.edu
CV | Google Scholar | LinkedIn
News
2021/06/30: Our paper iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes is accepted at IROS 2021!
2021/06/20: We concluded the iGibson Challenge 2021 and the CVPR2021 Embodied AI Workshop. Check out our video for the challenge and the panel discussions that I was a part of.
2021/02/28: Our paper ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation is accepted at ICRA 2021!
2021/02/17: We launched the iGibson Challenge 2021 at the CVPR2021 Embodied AI Workshop! We featured Interactive and Social Navigation in dynamic environments in iGibson.
Education
Stanford University
Ph.D. Candidate in Computer Science
2020/09 - Present
Stanford, CA
Stanford University
Master of Science in Management Science and Engineering
2019/01 - 2020/06
Stanford, CA
GPA: 4.0 / 4.0
Stanford University
Bachelor of Science in Computer Science with Distinction
2013/09 - 2017/06
Stanford, CA
GPA: 3.93 / 4.0
Publications
iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes
Chengshu Li*, Bokui Shen*, Fei Xia*, Roberto Martín-Martín*, Linxi Fan, Guanzhi Wang, Shyamal Buch, Claudia D'Arpino, Sanjana Srivastava, Lyne P. Tchapmi, Micael E. Tchapmi, Kent Vainio, Li Fei-Fei, Silvio Savarese
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021
Interactive Gibson: A Benchmark for Interactive Navigation in Cluttered Environments
Fei Xia, William B. Shen, Chengshu Li, Priya Kasimbeg, Micael Tchapmi, Alexander Toshev, Roberto Martín-Martín, Silvio Savarese
IEEE Robotics and Automation Letters (RA-L) and International Conference on Robotics and Automation (ICRA) 2020
* denotes equal contribution
Industry Experience
Google Brain Robotics
Student Researcher (Part-time)
2019/11 - 2020/05
Mountain View, CA
AutoX, Inc.
Research Engineer for Deep Learning and Perception
2017/09 - 2018/12
San Jose, CA
Shift Technologies, Inc.
Software Engineering Intern
2016/06 - 2016/09
San Francisco, CA
Tableau Software
Software Engineering Intern
2015/06 - 2015/09
Seattle, WA
Tech Reports
Indexed Value Function Learning via Distributional Temporal Difference
Tian Tan*, Zhihan Xiong*, Chengshu Li*
[paper]
DeepShuai: Deep Reinforcement Learning based Chinese Chess Player
Chengshu Li*, Kedao Wang*, Zihua Liu*
[paper]
Effective Word Representation for Named Entity Recognition
Jun-Ting Hsieh*, Chengshu Li*, Wendi Liu*
[paper]
* denotes equal contribution