Title: Asking for Help with the Right Question by Predicting Human
Visual Performance

Speaker: Hong Cai (UC Santa Barbara)

Abstract: In this talk, we consider robotic surveillance tasks that
involve visual perception. The robot has a limited access to a remote
operator to ask for help. However, humans may not be able to accomplish
the visual task in many scenarios, depending on the sensory input. We
propose a machine learning-based approach that allows the robot to
probabilistically predict human visual performance for any visual
input. Based on this prediction, we then present a methodology that
allows the robot to properly optimize its field decisions in terms of
when to ask for help, when to sense more, and when to rely on itself.
The proposed approach enables the robot to ask the right questions,
only querying the operator with the sensory inputs for which humans
have a high chance of success. Furthermore, it allows the robot to
autonomously locate the areas that need more sensing. We then run a
number of robotic surveillance experiments on our campus as well as a
larger-scale evaluation with real data/human feedback in a simulation
environment.