activity
20162022
most citedOn the Utility of Learning about Humans for Human-AI Coordination

91 citations · 226 across the 28 of their papers we have counts for

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10 papers · 1 filter

cs.AI20227 cited

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…

cs.AI2021

Dynamically Switching Human Prediction Models for Efficient Planning

Arjun Sripathy, Andreea Bobu, Daniel S. Brown +1

As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to res…

cs.AI2021

On complementing end-to-end human behavior predictors with planning

Liting Sun, Xiaogang Jia, Anca D. Dragan

High capacity end-to-end approaches for human motion (behavior) prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of di…

cs.AI20214 cited

Choice Set Misspecification in Reward Inference

Rachel Freedman, Rohin Shah, Anca Dragan

Specifying reward functions for robots that operate in environments without a natural reward signal can be challenging, and incorrectly specified rewards can incentivise degenerate…

cs.AI2020

AvE: Assistance via Empowerment

Yuqing Du, Stas Tiomkin, Emre Kiciman +3

One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on i…

cs.AI20193 cited

Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning

Smitha Milli, Anca D. Dragan

It is incredibly easy for a system designer to misspecify the objective for an autonomous system ("robot''), thus motivating the desire to have the robot learn the objective from h…