Patrick Quinn
Ph.D. Student
Bio
Thesis title: End-to-End Imitation Learning for Optimal Asteroid Proximity Operations
Research Area: Machine Learning for End-to-End Controls, with a focus on improving the safety of ML-based controllers. Currently working on Imitation Learning for Optimal End-to-End Asteroid Proximity Operations, where a model is trained to output optimal control commands from raw sensor data for navigation near asteroids.
Expected graduation date: MS in Aerospace Engineering in Fall of 2024 - PhD in Aerospace Engineering in Spring of 2028
2D Histogram for analyzing correlation between optimal and generated control outputs.
Visualization of lidar sensors in Gazebo simulation environment
