Optimizing Algorithms from Pairwise User Preferences
Leonid Keselman,Katherine Shih,Martial Hebert,Aaron Steinfeld,Leonid Keselman,Katherine Shih,Martial Hebert,Aaron Steinfeld
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 impl...
