Available at: https://digitalcommons.calpoly.edu/theses/3216
Date of Award
12-2025
Degree Name
MS in Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Carlos Diaz Alvarenga
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
Classical techniques in autonomous navigation struggle in tightly constrained spaces. Machine learning has been shown to perform better in these difficult environments but most techniques require large amounts of navigation experience for training. Using a new machine learning paradigm learning from hallucination (LfH), training data can be collected in a safe environment and not require supervision. Data is collected in real time while an agent performs a random walk in free space, supervision is not required as there are no obstacles for the robot to run into. After a random walk a post processing pipeline will hallucinate a safety corridor around the path the robot performed during its random walk. This will trick a learning policy into believing the labels are a result of avoiding an obstacle. Our experiments show that variations in the creation of the safety corridor can improve model performance. Our approach enables a robot to navigate previously unseen environments with real obstacles despite only being trained in a simulated environment, alleviating the difficulties of real world demonstrations.