Optimizing Algorithms From Pairwise User Preferences

TL;DR

This paper introduces SortCMA, a method for optimizing algorithm parameters based on pairwise user preferences, especially useful when explicit metrics are unavailable, demonstrated on robot sensor tuning and social navigation.

Contribution

The paper presents SortCMA, a novel approach that optimizes parameters using pairwise preferences without modeling explicit rewards, suitable for high-dimensional problems.

Findings

Successfully optimized a depth sensor without ground truth

Improved robot social navigation aligning with user preferences

User study validated effectiveness of the approach

Abstract

Typical black-box optimization approaches in robotics focus on learning from metric scores. However, that is not always possible, as not all developers have ground truth available. Learning appropriate robot behavior in human-centric contexts often requires querying users, who typically cannot provide precise metric scores. Existing approaches leverage human feedback in an attempt to model an implicit reward function; however, this reward may be difficult or impossible to effectively capture. In this work, we introduce SortCMA to optimize algorithm parameter configurations in high dimensions based on pairwise user preferences. SortCMA efficiently and robustly leverages user input to find parameter sets without directly modeling a reward. We apply this method to tuning a commercial depth sensor without ground truth, and to robot social navigation, which involves highly complex preferences over robot behavior. We show that our method succeeds in optimizing for the user's goals and perform a user study to evaluate social navigation results.