  In this paper, we study the task of embodied interactive learning for object
detection. Given a set of environments (and some labeling budget), our goal is
to learn an object detector by having an agent select what data to obtain
labels for. How should an exploration policy decide which trajectory should be
labeled? One possibility is to use a trained object detector's failure cases as
an external reward. However, this will require labeling millions of frames
required for training RL policies, which is infeasible. Instead, we explore a
self-supervised approach for training our exploration policy by introducing a
notion of semantic curiosity. Our semantic curiosity policy is based on a
simple observation -- the detection outputs should be consistent. Therefore,
our semantic curiosity rewards trajectories with inconsistent labeling behavior
and encourages the exploration policy to explore such areas. The exploration
policy trained via semantic curiosity generalizes to novel scenes and helps
train an object detector that outperforms baselines trained with other possible
alternatives such as random exploration, prediction-error curiosity, and
coverage-maximizing exploration.
