35 citations · 140 across the 26 of their papers we have counts for
4 papers · 1 filter
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…
Flexible Agent Alignment with Goal Inference from Open-Ended Dialog
Rachel Ma, Jingyi Qu, Andreea Bobu +1
We introduce Open-Universe Assistance Games (OU-AGs), a formal framework extending assistance games to LLM-based agents. Effective assistance requires reasoning over human preferen…
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…