Nina Moorman — accepted AI-conference papers

2023

Athletic Mobile Manipulator System for Robotic Wheelchair Tennis

Zulfiqar Zaidi, Daniel Martin, Nathaniel Belles, Viacheslav Zakharov, Arjun Krishna, Kin Man Lee +17

RA-L 2023

Athletics are a quintessential and universal expression of humanity. From French monks who in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$12{\text{th}}$</tex-math></inline-formula> century invented <italic

Cited by 25 Source ↗code ↗Scholar ↗

2022

LanCon-Learn: Learning With Language to Enable Generalization in Multi-Task Manipulation

Andrew Silva, Nina Moorman, William Silva, Zulfiqar Zaidi, Nakul Gopalan, Matthew C. Gombolay

RA-L 2022

Robots must be capable of learning from previously solved tasks and generalizing that knowledge to quickly perform new tasks to realize the vision of ubiquitous and useful robot assistance in the real world. While multi-task learning research has produced agents capable of performing multiple tasks,

Cited by 39 Source ↗Scholar ↗

2022

Negative Result for Learning from Demonstration: Challenges for End-Users Teaching Robots with Task And Motion Planning Abstractions

Nakul Gopalan, Nina Moorman, Manisha Natarajan, Matthew Gombolay

RSS 2022poster

Learning from demonstration (LfD) seeks to democratize robotics by enabling non-experts to intuitively program robots to perform novel skills through human task demonstration. Yet, LfD is challenging under a task and motion planning setting which requires hierarchical abstractions. Prior work has st…

Cited by 11 Source ↗PDF ↗Scholar ↗