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…
Human-Guided Harm Recovery for Computer Use Agents
Christy Li, Sky CH-Wang, Andi Peng +1
As LM agents gain the ability to execute actions on real computer systems, we need ways to not only prevent harmful actions at scale but also effectively remediate harm when preven…
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…
Goal Inference from Open-Ended Dialog
Rachel Ma, Jingyi Qu, Andreea Bobu +1
Embodied AI Agents are quickly becoming important and common tools in society. These embodied agents should be able to learn about and accomplish a wide range of user goals and pre…