Learning structured interaction models for robot navigation in human environments

Robots are becoming increasingly prevalent in our daily lives. However, these autonomous agents work best in isolation. Integrating robots into human environments remains challenging due to the complex and dynamic nature of human-robot interactions. This thesis addresses the challenge of robot navigation in human environments by proposing structured learning methods that enhance robots’ ability to interact with humans in a safe, efficient, and socially aware manner. We explore two types of interactions: implicit interactions through motion and explicit interactions through language. We build navigation systems that predict human intentions, reason about subtle interactions among agents, and plan paths for robots to fulfill tasks. In previous work, model-based approaches and end-to-end learning methods have demonstrated their own advantages and limitations. To combine the best of both worlds, this thesis develops structured ML systems by incorporating the predictions of human behaviors into robot planning. In addition, we formulate interactive scenarios using graph structures, which enables robots to achieve a more nuanced understanding of human behavior and interaction dynamics. We propose intention and interaction-aware robot decision-making systems for navigation in human spaces. Through simulation benchmarks, real world experiments, and user studies, we demonstrate robot’s capability to navigate in various complex human environments. Our contributions span three applications: (1) driver trait inference for autonomous vehicle navigation at T-intersections, (2) crowd navigation using attention-based spatio-temporal graphs, and (3) assistive navigation for persons with visual impairments using dialogue and visual-language grounding. Through these applications, we demonstrate the effectiveness of structured learning in robotic tasks that involve human-robot interaction. This thesis proposes algorithms and tools for deploying robots in real-world human settings, advancing the field of learning-based human-robot interaction.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Liu, Shuijing

Contributors dc:contributor

• Driggs-Campbell, Katherine
• Amato, Nancy M.
• Hauser, Kris
• Gupta, Saurabh

• Robotics
• Machine Learning
• Human-robot Interaction
• Robot Navigation
• Artificial Intelligence.

Statement dc:rights

• Copyright 2024 Shuijing Liu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125626

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125626