Towards Improving User Expectations of Robots by Leveraging Their Experience With Computer Vision Apps

摘要

This paper explores whether experiential knowledge of computer vision from interacting with daily apps (e.g., Instagram, Zoom, etc.) can be leveraged to improve users’ expectations of robotic capabilities. We evaluate users’ ability to predict when computer vision apps might fail and if they can apply their experience to reason about computer vision in robotic systems. We show that although users can reliably predict computer vision app capabilities and functionality, they tend to ascribe human-level knowledge to those apps and do not reliably correlate app functionality with similar robotic tasks. We propose that experiential knowledge gained through interaction with software apps is a potential way to “calibrate” user expectations of the function and failure states of complex systems.