Sikes, Kelsey, authorSreedharan, Sarath, advisorBlanchard, Nathaniel, committee memberChong, Edwin K.P., committee member2025-09-012025-09-012025https://hdl.handle.net/10217/241759https://doi.org/10.25675/3.02079At the core of most successful human-robot collaborations is alignment between a robot's behavior and a human's expectations. Achieving this alignment is often difficult, however, because without careful specification, a robot may misinterpret a human's goals, causing it to perform actions with unexpected, if not dangerous side effects. To avoid this, I propose a new metric called Goal State Divergence (GSD), which represents the difference between the final goal state achieved by a robot and the one a human user expected. In cases where GSD cannot be directly calculated, I show how it can be approximated using maximal and minimal bounds. I then leverage GSD in my novel human-robot goal alignment design (HRGAD) problem, which identifies a minimal set of environment modifications that can reduce such mismatches. To illustrate the effectiveness of my method for reducing goal state divergence, I then empirically evaluate it on several standard planning benchmarks.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.classical planninggoal recognitionplanning and schedulingenvironment designautomated planninghuman-robot interactionText