Leveraging Foundation Models for Enhancing Robot Perception and Action
This work addresses fundamental challenges in robotics for enabling more effective robot operations, but it appears incremental as it builds on existing foundation models.
This thesis tackled the problem of improving robot perception and action in unstructured environments by leveraging foundation models, resulting in a cohesive framework for semantics-aware robotic intelligence.
This thesis investigates how foundation models can be systematically leveraged to enhance robotic capabilities, enabling more effective localization, interaction, and manipulation in unstructured environments. The work is structured around four core lines of inquiry, each addressing a fundamental challenge in robotics while collectively contributing to a cohesive framework for semantics-aware robotic intelligence.