91 citations · 242 across the 32 of their papers we have counts for
9 papers · 1 filter
Learning Representations that Enable Generalization in Assistive Tasks
Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown +2
Recent work in sim2real has successfully enabled robots to act in physical environments by training in simulation with a diverse ''population'' of environments (i.e. domain randomi…
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking
David Zhang, Micah Carroll, Andreea Bobu +1
One of the most successful paradigms for reward learning uses human feedback in the form of comparisons. Although these methods hold promise, human comparison labeling is expensive…
UniMASK: Unified Inference in Sequential Decision Problems
Micah Carroll, Orr Paradise, Jessy Lin +8
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same…
Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration
Mesut Yang, Micah Carroll, Anca Dragan
AI agents designed to collaborate with people benefit from models that enable them to anticipate human behavior. However, realistic models tend to require vast amounts of human dat…
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models
Cassidy Laidlaw, Anca Dragan
Models of human behavior for prediction and collaboration tend to fall into two categories: ones that learn from large amounts of data via imitation learning, and ones that assume…
Inferring Rewards from Language in Context
Jessy Lin, Daniel Fried, Dan Klein +1
In classic instruction following, language like "I'd like the JetBlue flight" maps to actions (e.g., selecting that flight). However, language also conveys information about a user…