5 papers
QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents
Jordan Abi Nader, David Lee, Nathaniel Dennler +1
Robots must learn from both what people do and what they say, but either modality alone is often incomplete: physical corrections are grounded but ambiguous in intent, while langua…
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…
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…
The Current State of AI Bias Bounties: An Overview of Existing Programmes and Research
Sergej Kucenko, Nathaniel Dennler, Fengxiang He
Current bias evaluation methods rarely engage with communities impacted by AI systems. Inspired by bug bounties, bias bounties have been proposed as a reward-based method that invo…