Asking Rank Queries in Pose Learning

This paper presents a system in which a robot uses Active Learning (AL) to improve its learning capabilities for pose recognition. We propose a sub-type of Feature Queries, Rank Queries (RQ), in which the user states the relevance of a characteristic of the learning space. In the case of pose learning, these queries refer to the relevance of a single limb for a certain pose. We test the use of RQ with 24 users to learn 3 pointing poses and compare the learning accuracy against a passive learning approach. Our results show that RQ can increase the robot's learning accuracy.