91 citations · 242 across the 32 of their papers we have counts for
6 papers · 1 filter
Assisted Perception: Optimizing Observations to Communicate State
Siddharth Reddy, Sergey Levine, Anca D. Dragan
We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairments, where users may have systematic biases that lead to…
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…
Feature Expansive Reward Learning: Rethinking Human Input
Andreea Bobu, Marius Wiggert, Claire Tomlin +1
When a person is not satisfied with how a robot performs a task, they can intervene to correct it. Reward learning methods enable the robot to adapt its reward function online base…
Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections
Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac +2
Human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses…
Reward-rational (implicit) choice: A unifying formalism for reward learning
Hong Jun Jeon, Smitha Milli, Anca D. Dragan
It is often difficult to hand-specify what the correct reward function is for a task, so researchers have instead aimed to learn reward functions from human behavior or feedback. T…
LESS is More: Rethinking Probabilistic Models of Human Behavior
Andreea Bobu, Dexter R. R. Scobee, Jaime F. Fisac +2
Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision m…