AI-HRIシンポジウム2018論文集
Proceedings of the AI-HRI Symposium at AAAI-FSS 2018
人とロボットのインタラクションを通じた対話的学習の研究を集めたAAAI-FSS 2018シンポジウムの論文集で、HRIがAI研究を前進させる重要な領域であることを論じている。
著者: Kalesha Bullard, Nick DePalma, Richard G. Freedman, Bradley Hayes, Luca Iocchi, Katrin Lohan, Ross Mead, Emmanuel Senft, Tom Williams
分類: cs.RO
原文アブストラクト
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.
