I'm currently a PhD student in the Computer Science Department at Stanford University.

I graduated with a B.S. in Electrical Engineering & Computer Science from UC Berkeley, advised by Prof. Ken Goldberg at the Berkeley AUTOLAB and the Berkeley AI Research Lab.

BAIR Blog on robust imitation learning.

Preprints

• Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning.
  Jonathan Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg.
  arXiv.org, 2019.


• Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization.
  Jonathan Lee*, Aldo Pacchiano*, Michael I. Jordan.
  arXiv.org, 2019.


• Online Learning with Continuous Variations: Dynamic Regret and Reductions.
  Ching-An Cheng*, Jonathan Lee*, Ken Goldberg, Byron Boots.
  arXiv.org, 2019.

Conference publications

• On-Policy Robot Imitation Learning from a Converging Supervisor.
  Ashwin Balakrishna*, Brijen Thananjeyan*, Jonathan Lee, Arsh Zahed, Felix Li, Joseph E. Gonzalez, Ken Goldberg.
  Conference on Robot Learning (CoRL), 2019.


• A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning.
  Jonathan Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg.
  Springer Proceedings in Advanced Robotics: Algorithmic Foundations of Robotics, 2019.
  International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018.


• Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models.
  Ajay Kumar Tanwani, Jonathan Lee, Brijen Thananjeyan, Michael Laskey, Sanjay Krishnan, Roy Fox, Ken Goldberg, Sylvain Calinon
  Springer Proceedings in Advanced Robotics: Algorithmic Foundations of Robotics, 2019.
  International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018.


• Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations.
  Jonathan Lee, Michael Laskey, Roy Fox, Ken Goldberg.
  IEEE Conference on Automation Science and Engineering (CASE), 2018.


• DART: Noise Injection for Robust Imitation Learning.
  Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg.
  Conference on Robot Learning (CoRL), 2017.


• Comparing Human-Centric and Robot-Centric Sample Efficiency for Robot Deep Learning from Demonstrations.
  Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg.
  IEEE Conference on Robotics and Automation (ICRA), 2017.


• Robot Grasping in Clutter: Using a Hierarchy of Supervisors for Learning from Demonstrations.
  Michael Laskey, Jonathan Lee, Caleb Chuck, David Gealy, Wesley Hsieh, Florian T. Pokorny, Anca D. Dragan, and Ken Goldberg.
  IEEE Conference on Automation Science and Engineering (CASE), 2016.

Short papers, workshop papers, etc.

• Stability Analysis of On-Policy Imitation Learning Algorithms Using Dynamic Regret.
  Jonathan Lee, Michael Laskey, Ajay Kumar Tanwani, Ken Goldberg.
  RSS Workshop on Imitation and Causality, 2018.


• Iterative Noise Injection for Scalable Imitation Learning.
  Michael Laskey, Jonathan Lee, Wesley Hsieh, Richard Liaw, Jeffrey Mahler, Roy Fox, Ken Goldberg.
  arXiv, 2017.