Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature Queries
This work addresses the challenge of efficiently learning user preferences for robots, though it is incremental as it builds on existing comparison-based methods.
The paper tackles the problem of learning a robot's objective function by augmenting comparison queries with feature queries, which are easier for users to provide than demonstrations. The result shows that this approach extracts information faster and leads to robots better matching user preferences in behavior.
We focus on learning the desired objective function for a robot. Although trajectory demonstrations can be very informative of the desired objective, they can also be difficult for users to provide. Answers to comparison queries, asking which of two trajectories is preferable, are much easier for users, and have emerged as an effective alternative. Unfortunately, comparisons are far less informative. We propose that there is much richer information that users can easily provide and that robots ought to leverage. We focus on augmenting comparisons with feature queries, and introduce a unified formalism for treating all answers as observations about the true desired reward. We derive an active query selection algorithm, and test these queries in simulation and on real users. We find that richer, feature-augmented queries can extract more information faster, leading to robots that better match user preferences in their behavior.