Scholar
Nina Moorman
Google Scholar ID: KIjmNRMAAAAJ
Georgia Institute of Technology
Citations & Impact
Citations
165
H-index
7
i10-index
6
Publications
20
Co-authors
23
list available
Publications
Resume (English only)
Academic Achievements
- ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics (ICLR 2025)
- Investigating the Impact of Experience on a User's Ability to Perform Hierarchical Abstraction (IJRR 2024, RSS 2023 Best Paper Nominee)
- Negative Result for Learning from Demonstration: Challenges for End-Users Teaching Robots with Task and Motion Planning Abstractions (RSS 2022)
- Lancon-Learn: Learning with Language to Enable Generalization in Multi-Task Manipulation (RA-L 2022, presented at ICRA 2022)
- Impacts of Robot Learning on User Attitude and Behavior (HRI 2023)
- Mind Meld: Personalized Meta-Learning for Robot-Centric Imitation Learning (HRI 2022 Best Technical Paper Award)
Research Experience
- Involved in several research projects related to robot learning, including combining natural language guidance with visual user demonstrations to better align robot behavior with user intentions, etc.
Education
- Degree: Ph.D. student (in progress); University: Georgia Tech CORE Robotics Lab; Advisor: Professor Matthew Gombolay; Year: Fifth year; Major: Computer Science. Undergraduate: Graduated from Georgia Tech in 2021 with a B.S. in Computer Science.
Background
- Research Interests: Human-interactive robot learning and care robotics. Bio: Through her research, she develops algorithms that enable robots to learn in-situ, from non-expert user demonstrators.
