Rachel Luo

Rachel Luo


Rachel Luo is a Ph.D. candidate in the Electrical Engineering department. She received a B.S. in Electrical Engineering and Computer Science from MIT in 2014, and an M.S. in Electrical Engineering from Stanford in 2017. Rachel’s research focuses on uncertainty quantification for problems at the intersection of computer vision and robotics.

In her free time, Rachel enjoys photography, rock climbing, hiking, and commuting by electric longboard.

Awards:

  • Stanford Graduate Fellowship
  • National Science Foundation (NSF) Fellowship

ASL Publications

  1. R. Luo, S. Zhao, J. Kuck, B. Ivanovic, S. Savarese, E. Schmerling, and M. Pavone, “Sample-Efficient Safety Assurances using Conformal Prediction,” in Proc. IEEE Conf. on Robotics and Automation, 2022. (Submitted)

    Abstract: When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe situation is imminent (in the absence of corrective action). To reliably improve safety, these warning systems should have a provable false negative rate; i.e., of the situations than are unsafe, fewer than epsilon will occur without an alert. In this work, we present a framework that combines a statistical inference technique known as conformal prediction with a simulator of robot/environment dynamics, in order to tune warning systems to provably achieve an epsilon false negative rate using as few as 1/epsilon data points. We apply our framework to a driver warning system and a robotic grasping application, and empirically demonstrate guaranteed false negative rate and low false detection (positive) rate using very little data.

    @inproceedings{LuoZhaoEtAl2022,
      author = {Luo, R. and Zhao, S. and Kuck, J. and Ivanovic, B. and Savarese, S. and Schmerling, E. and Pavone, M.},
      title = {Sample-Efficient Safety Assurances using Conformal Prediction},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2022},
      month = may,
      note = {Submitted},
      keywords = {sub},
      owner = {rsluo},
      timestamp = {2021-09-20},
      url = {https://arxiv.org/abs/2109.14082}
    }