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Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
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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; however, they do not consider how easy it will be for the human to answer! In this paper we explore an information gain formulation for optimally selecting questions that naturally account for the human's ability to answer. Our approach identifies questions that optimize the trade-off between robot and human uncertainty, and determines when these questions become redundant or costly. Simulations and a user study show our method not only produces easy questions, but also ultimately results in faster reward learning.
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Cited by 3 Pith papers
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Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots
Combining CMA-ES sampling with information gain query selection improved perceived behavioral adaptation and overall preference in a 14-person study, though ease-of-use gains over pure information gain were not significant.
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