Events & News

May 2021 - Our paper on a unifying framework, efficient reductions, and practical algorithms for imitation learning was accepted to ICML ‘21! You can check our video explanations and code here.

September 2020 - I started a PhD at CMU’s Robotics Institute!

May 2020 - I finished up my M.S. at UC Berkeley, with more than a little help from my wonderful friends and collaborators!

Research Highlights

Of Moments and Matching: Trade-offs and Treatments in Imitation Learning

We construct a taxonomy for imitation learning algorithms, derive bounds for each class, construct novel reduction-based algorithmic templates that achieve these bounds, and implement simple elegant realizations with competitive emperical performance. Published at ICML '21. [Website]

Scaled Autonomy

As fleet sizes grow, it becomes difficult for a single teleoperator to supervise all robots. We learn from user preferences to make automated switches. Our work was published at ICRA 2020.

On the Utility of Model Learning in HRI

We compare three methods of modeling human driving behavior on their sample efficiency and transferability.