CV       Education       Publications       Outreach       Presentations
Email: kyle.hatch@tri.global

I am an AI Resident in the Robotics Division at the Toyota Research Institute (TRI). I plan to pursue a PhD in Computer Science, Machine Learning, or Robotics. My research interests lie primarily within machine learning, robotics, and reinforcement learning (RL). Within robotics, I am especially excited about training large-scale models on human video data from the Internet in order to provide robots with common sense reasoning abilities. I am also interested in using machine learning to help solve problems in healthcare and renewable energy. Previously, I was a student at Stanford, where I studied Computer Science (B.S. with honors and M.S.).

I am extremely fortunate to have worked with many wonderful mentors during my time as a master’s and undergraduate student. I worked with Prof. Chelsea Finn in the Stanford IRIS Lab as an undergraduate and master’s student. I also worked with Prof. Mykel Kochenderfer as an undergraduate student in the Stanford Intelligent Systems Laboratory (SISL).

In my free time, I enjoy backpacking in the mountains, playing soccer, and watching horror movies.

Education

Stanford University                                                               Stanford, CA
M.S. in Computer Science                                               Graduated: June 2023
Artificial Intelligence Track                                                             GPA: 4.05
Coterminal Master’s Program

Stanford University                                                               Stanford, CA
B.S. in Computer Science with honors                                  Graduated: June 2022
Artificial Intelligence Track

Publications

Published/Accepted

Rafailov, R.*, Kolev, V.*, Hatch, K. B., Wu, J., and Finn, C., ”Efficient Imitation Learning with Conservative World Models,” Learning for Dynamics & Control Conference (L4DC), 2024.   PDF

Rafailov, R.*, Hatch, K. B.*, Singh, A., Smith, L., Kumar, A., Kostrikov, I., Hansen-Estruch, P., Kolev, V., Ball, P., Wu, J., Finn, C., and Levine, S., “D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning,” Reinforcement Learning Conference (RLC), 2024.   PDF

Rafailov, R.*, Hatch, K. B.*, Kolev, V., Martin, J., Phielipp, M., and Finn, C., ”MOTO: Offline to Online Fine-tuning for Model-Based Reinforcement Learning,” Conference on Robot Learning (CoRL), 2023.   PDF     Website

Hatch, K. B., Eysenbach, B., Yu, T., Rafailov, R., Salakhutdinov, R., Levine, S., and Finn, C., ”Contrastive Example-Based Control,” Learning for Dynamics & Control Conference (L4DC), 2023.   PDF     Website     Presentation (NeurIPS workshop version)

Zhou, G., Dean, V., Srirama, M. K., Rajeswaran, A., Pari, J., Hatch, K. B., Jain, A., Yu, T., Abbeel, P., Pinto, L., Finn, C., and Gupta, A., “Train Offline, Test Online: A Real Robot Learning Benchmark,” 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023.   PDF     Website

Mern, J., Hatch, K., Silva, R., Hickert, C., Sookoor, T., and Kochenderfer, M. J., “Autonomous Attack Mitigation for Industrial Control Systems,” 2022 IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2022, pp. 28–36.   PDF

Senanayake, R.*, Hatch, K.*, Zheng, J., and Kochenderfer, M. J., “3D Radar Velocity Maps for Uncertain Dynamic Environments,” IEEE International Conference on Intelligent Robots and Systems (IROS), 2021.   PDF     Presentation

Hatch, K., Mern, J., and Kochenderfer, M. J., “Obstacle Avoidance Using a Monocular Camera,” AIAA SciTech Forum, 2021.   PDF     Presentation

Under Review

Hatch, K.,, Balakrishna, A., Mees, O., Nair, S., Wulfe, B., Itkina, M., Eysenbach, B., Levine, S., Kollar, T., and Burchfiel, B., “GHIL-Glue: Hierarchical Control with Filtered Subgoal Images,” 2025 IEEE International Conference on Robotics and Automation (ICRA), 2025.   PDF     Website     Presentation

* denotes equal contribution

Outreach

Breakthrough Silicon Valley (BTSV)

November 2023 – April 2024

Volunteer Tutor

Breakthrough Silicon Valley is an organization that provides academic support to middle school and high school students who are on track to becoming first-generation college students. I primarily provide homework support to high school students with mathematics.

East Palo Alto Stanford Academy (EPASA)

October 2018 – March 2020

Volunteer Tutor

EPASA is a program run through Stanford University in which undergraduate students tutor middle school students who attend school in East Palo Alto. I provided homework support to seventh and eighth grade students in math and English, and also helped them to develop effective study skills.

Stanford 1st Ward Volunteer Tutoring Program

September 2017 – June 2019

Volunteer Tutor

The Stanford 1st Ward Volunteer Tutoring Program is a program run through a local religious organization that provides free tutoring to K-12 students from around the South San Francisco Bay Area. I provided homework support students in math, reading, and English.

Video Presentations

“GHIL-Glue: Hierarchical Control with Filtered Subgoal Images”

     

“Offline Example-Based Control,” NeurIPS Offline RL and Deep RL Workshops, 2022.

     

“3D Radar Velocity Maps for Uncertain Dynamic Environments,” IEEE International Conference on Intelligent Robots and Systems (IROS), 2021.

     

“Obstacle Avoidance Using a Monocular Camera,” AIAA SciTech Forum, 2021.