me
ANQI LI
Bill & Melinda Gates Center (CSE2) 252-7
University of Washington
Seattle, WA, 98195
email anqil4[at]cs.washington.edu
CV (updated November 2020)
Google Scholar

About

I am currently a PhD student in the Paul G. Allen School of Computer Science and Engineering (CSE) at the University of Washington, working with professor Byron Boots and Georgia Tech professor Magnus Egerstedt. My research aims to make robot learning safe and sample efficient. I approach this through encoding domain knowledge and problem structure, which are readily available in many robotics problems, using control theoretical tools. My specific research topics include safe reinforcement learning, learning stable motion policies, learning from human demonstrations, and planning & control with formal guarantees. My research is supported by the NVIDIA Graduate Fellowship.

I moved to the University of Washington from the Georgia Institute of Technology, where I was a Robotics PhD student. I received my Master's degree in Robotics from Carnegie Mellon University, where I was advised by professor Katia Sycara. Before that I got my Bachelor's degree in Automation from Zhejiang University, China. I spent two wonderful summers at NVIDIA Research working with Nathan Ratliff and Dieter Fox.

University of Washington Georgia Institute of Technology Carnegie Mellon University Zhejiang University
2019 - Present 2017 - 2019 2015 - 2017 2011 - 2015

Publications

Journal Publication

  1. P. Pierpaoli, A. Li, M. Srinivasan, X. Cai, S. Coogan, and M. Egerstedt, "A Sequential Composition Framework for Coordinating Multi-robot Behaviors." IEEE Transactions on Robotics (T-RO), 2020 (to appear) [ pdf forthcoming ]

Conference Publications

  1. M. A. Rana, A. Li, D. Fox, B. Boots, F. Ramos, and N. Ratliff, "Euclideanizing Flows: Diffeomorphic Reductions for Learning Stable Dynamical Systems." Conference on Learning for Dynamics and Control (L4DC), 2020 [ PDF ]
  2. A. Li, C. Cheng, B. Boots, and M. Egerstedt, "Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks." IEEE Conference on Decision and Control (CDC), 2019 [ PDF ]
  3. M. A. Rana*, A. Li*, H. Ravichandar, M. Mukadam, S. Chernova, D. Fox, B. Boots, and N. Ratliff, "Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations." Conference on Robot Learning (CoRL), 2019 [ PDF ]
  4. A. Li, M. Mukadam, M. Egerstedt, and B. Boots, "Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies." The International Symposium on Robotics Research (ISRR), 2019 [ PDF ]
  5. A. Li and M. Egerstedt, "On the Trade-Off Between Communication and Execution Overhead for Control of Multi-Agent Systems" American Control Conference (ACC), 2019 [ PDF ]
  6. A. Li, L. Wang, P. Pierpaoli, and M. Egerstedt, "Formally Correct Composition of Coordinated Behaviors Using Control Barrier Certificates" IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 [ PDF ]
  7. A. Li, W. Luo, S. Nagavalli, and K. Sycara, "Decentralized Coordinated Motion for a Large Team of Robots Preserving Connectivity and Avoiding Collisions." IEEE International Conference on Robotics and Automation (ICRA), 2017 [ PDF ]
  8. A. Li, W. Luo, S. Nagavalli, N. Chakraborty, and K. Sycara, "Handling State Uncertainty in Distributed Information Leader Selection for Robotic Swarms." IEEE Conference on System, Man, and Cybernetics (SMC), 2016 [ PDF ]
  9. A. Li, M. Lewis, C. Lebiere, K. Sycara, S. Khatib, Y. Tang, M. Siedsma, and D. Morrison, "A Computational Model Based on Human Performance for Fluid Management in Critical Care." IEEE Symposium Series on Computational Intelligence (SSCI), 2016 [ PDF ]

Workshop Paper

  1. A. Li*, C.-A. Cheng*, M. A. Rana, N. Ratliff, and B. Boot, "RMP2: a Differentiable Policy Class for Robotic Systems with Control-Theoretic Guarantees." Workshop on Robot Learning, Conference on Neural Information Processing Systems (NeurIPS Workshop), 2020 [ PDF ]

Thesis

  1. A. Li, "Decentralized Coordinated Motion for Robot Teams Preserving Connectivity and Avoiding Collisions." Master's Thesis, Carnegie Mellon University, 2017 [ PDF ]

Preprints

  1. N. Ratliff, K. Van Wyk, M. Xie, A. Li, and M. A. Rana, "Optimization Fabrics for Behavioral Design." arXiv:2010.15676 [ PDF ]
  2. M. Xie, K. Van Wyk, A. Li, M. A. Rana, D. Fox, B. Boots, and N. Ratliff, "Geometric Fabrics for the Acceleration-based Design of Robotic Motion." arXiv:2010.14750 [ PDF ]
  3. N. Ratliff, K. Van Wyk, M. Xie, A. Li, and M. A. Rana, "Generalized Nonlinear and Finsler Geometry for Robotics." arXiv:2010.14745 [ PDF ]
  4. N. Ratliff, K. Van Wyk, M. Xie, A. Li, and M. A. Rana, "Optimization Fabrics." arXiv:2008.02399 [ PDF ]
  5. P. Pierpaoli, H. Ravichandar, N. Waytowich, A. Li, D. Asher, and M. Egerstedt, "Inferring and Learning Multi-Robot Policies by Observing an Expert." arXiv:1909.07887 [ PDF ]

Selected Honors


All content copyright 2020 Anqi Li unless otherwise noted.