  The goal of the Interactive Learning for Artificial Intelligence (AI) for
Human-Robot Interaction (HRI) symposium is to bring together the large
community of researchers working on interactive learning scenarios for
interactive robotics. While current HRI research involves investigating ways
for robots to effectively interact with people, HRI's overarching goal is to
develop robots that are autonomous while intelligently modeling and learning
from humans. These goals greatly overlap with some central goals of AI and
interactive machine learning, such that HRI is an extremely challenging problem
domain for interactive learning and will elicit fresh problem areas for
robotics research. Present-day AI research still does not widely consider
situations for interacting directly with humans and within human-populated
environments, which present inherent uncertainty in dynamics, structure, and
interaction. We believe that the HRI community already offers a rich set of
principles and observations that can be used to structure new models of
interaction. The human-aware AI initiative has primarily been approached
through human-in-the-loop methods that use people's data and feedback to
improve refinement and performance of the algorithms, learned functions, and
personalization. We thus believe that HRI is an important component to
furthering AI and robotics research.
