Learning for open-world mobile robots

Wasserman, Justin

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https://hdl.handle.net/2142/129393 Copy

Description

Title

Learning for open-world mobile robots

Author(s)

Wasserman, Justin

Issue Date

2025-04-15

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

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.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129393

Copyright and License Information

Copyright 2025 Justin Wasserman

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