Kevin Huang

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Hi! I am a second-year PhD student at the University of Washington, advised by Byron Boots and Abhishek Gupta, researching robotics, machine learning, and optimal control. Broadly, I am interested in developing end-to-end intelligent, embodied systems that continuously learn from their environment, as well as robust control and planning in uncertain and dynamic environments.

Previously, I completed my undergrad at Caltech and am fortunate to have worked with Yisong Yue and Anima Anandkumar at Caltech, and Pablo Moscato at the University of Newcastle.

selected publications

  1. datt.gif
    DATT: Deep adaptive trajectory tracking for quadrotor control
    Kevin Huang, Rwik Rana, Alex Spitzer, and 2 more authors
    In Conference on Robot Learning, 2023
    Code Website
    16 Google Scholar citations
  2. dppo.png
    Deep Model Predictive Optimization
    Jake Sacks, Rwik Rana, Kevin Huang, and 3 more authors
    In IEEE International Conference on Robotics and Automation (ICRA), 2023
    3 Google Scholar citations
  3. grad.png
    Automatic Gradient Descent: Deep Learning without Hyperparameters
    Jermey Bernstein*, Chris Mingard*, Kevin Huang, and 2 more authors
    arXiv preprint arXiv:2304.05187, 2023
    11 Google Scholar citations
  4. cem-gd.png
    CEM-GD: Cross Entropy Method with Gradient Descent for Model Based Reinforcement Learning
    Kevin Huang, Sahin Lale, Rosalia Ugo, and 2 more authors
    arXiv preprint arXiv:2112.07746, 2021
    Blog
    9 Google Scholar citations
  5. sim2real.png
    Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
    Andrew Wagenmaker, Kevin Huang, Liyiming Ke, and 3 more authors
    In Advances in Neural Information Processing Systems, 2024
    0 Google Scholar citations