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cs.RO2026
Masked IRL: LLM-Guided Reward Disambiguation from Demonstrations and Language
Minyoung Hwang, Alexandra Forsey-Smerek, Nathaniel Dennler +1
Robots can adapt to user preferences by learning reward functions from demonstrations, but with limited data, reward models often overfit to spurious correlations and fail to gener…
cs.RO2026
GIFT: Generalizing Intent for Flexible Test-Time Rewards
Fin Amin, Nathaniel Dennler, Andreea Bobu
Robots learn reward functions from user demonstrations, but these rewards often fail to generalize to new environments. This failure occurs because learned rewards latch onto spuri…
cs.RO2026
Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG
Nathaniel Dennler, Zhonghao Shi, Yiran Tao +3
Robots that interact with humans must adapt to individual users' preferences to operate effectively in human-centered environments. An intuitive and effective technique to learn no…