Director of Research (if dissertation) or Advisor (if thesis)
Chowdhary, Girish
Doctoral Committee Chair(s)
Chowdhary, Girish
Committee Member(s)
Driggs-Campbell, Katie
Schwing, Alexander
Wang, Shenlong
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
robotics
embodied ai
simulator
navigation
open-world
artificial intelligence
computer vision
machine learning
Language
eng
Abstract
Deploying robots from a lab setting to open-world environments requires understanding semantic information and leveraging large data sources. Various paradigms have been introduced to tackle semantic visual-goal navigation, where an agent is placed in a random environment and must reach a goal. We first decompose this task into two components: (1) a data-driven exploration policy that learns semantics and environmental relations and (2) a geometric-based policy specialized for goal-directed navigation. Beyond this decomposition, we investigate whether further structure can enhance performance. To this end, we retrain the exploration policy with guidance from the geometric policy. Additionally, we explore a sim-to-real approach to improve state estimation for legged robots, enabling robust odometry prediction across diverse scenarios. This thesis presents real-world experiments supporting each of these works in mobile robotics. Future work is finally discussed towards the development of a foundation model for navigation.
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