91 citations · 242 across the 32 of their papers we have counts for
23 papers · 1 filter
Human-AI Learning Performance in Multi-Armed Bandits
Ravi Pandya, Sandy H. Huang, Dylan Hadfield-Menell +1
People frequently face challenging decision-making problems in which outcomes are uncertain or unknown. Artificial intelligence (AI) algorithms exist that can outperform humans at…
Learning from Extrapolated Corrections
Jason Y. Zhang, Anca D. Dragan
Our goal is to enable robots to learn cost functions from user guidance. Often it is difficult or impossible for users to provide full demonstrations, so corrections have emerged a…
A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance
Andrea Bajcsy, Sylvia L. Herbert, David Fridovich-Keil +4
Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents,…
Learning under Misspecified Objective Spaces
Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac +1
Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human's desired objective lies within the rob…
Establishing Appropriate Trust via Critical States
Sandy H. Huang, Kush Bhatia, Pieter Abbeel +1
In order to effectively interact with or supervise a robot, humans need to have an accurate mental model of its capabilities and how it acts. Learned neural network policies make t…
Hierarchical Game-Theoretic Planning for Autonomous Vehicles
Jaime F. Fisac, Eli Bronstein, Elis Stefansson +3
The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual de…