25 citations · 51 across the 10 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
Learning Contextually-Adaptive Rewards via Calibrated Features
Alexandra Forsey-Smerek, Julie Shah, Andreea Bobu
A key challenge in reward learning from human input is that desired agent behavior often changes based on context. For example, a robot must adapt to avoid a stove once it becomes…
Adaptive Language-Guided Abstraction from Contrastive Explanations
Andi Peng, Belinda Z. Li, Ilia Sucholutsky +4
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations. To learn a good reward, it is necessary to determine which features of th…
Inducing Structure in Reward Learning by Learning Features
Andreea Bobu, Marius Wiggert, Claire Tomlin +1
Reward learning enables robots to learn adaptable behaviors from human input. Traditional methods model the reward as a linear function of hand-crafted features, but that requires…