54 citations · 57 across the 3 of their papers we have counts for
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cs.RO2019★ 54 cited
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi +2
Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response;…
cs.RO2019★ 1 cited
Learning Reward Functions by Integrating Human Demonstrations and Preferences
Malayandi Palan, Nicholas C. Landolfi, Gleb Shevchuk +1
Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses…