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Nina Marie Moorman

2 accepted papers

2025

ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics

ICML 2025poster

Reinforcement learning (RL) has demonstrated compelling performance in robotic tasks, but its success often hinges on the design of complex, ad hoc reward functions. Researchers have explored how Large Language Models (LLMs) could enable non-expert users to specify reward functions more easily. Howe…

Cited by 0SourcePDFScholar
2025

Learning Interpretable Features from Interventions

RSS 2025poster

The behavior of in-home robots must be adaptable to end-users to adequately address individual users’ needs and preferences. Learning from Demonstration (LfD) is a common approach for customizing robot behavior, enabling non-expert users to teach robots how to perform tasks according to their prefer…

Cited by 0PDFScholar