人間-ロボット相互作用における自然な行動を引き出すためのデータ収集バイアス最小化への考察
Considerations for Minimizing Data Collection Biases for Eliciting Natural Behavior in Human-Robot Interaction
人間-ロボット相互作用研究で、未知の未知によるデータバイアスを減らし自然な行動を引き出すための予備研究と教訓を共有する。
著者: Snehesh Shrestha, Ge Gao, Cornelia Fermuller, Yiannis Aloimonos
分類: cs.RO
原文アブストラクト
Many of us researchers take extra measures to control for known-unknowns. However, unknown-unknowns can, at best, be negligible, but otherwise, they could produce unreliable data that might have dire consequences in real-life downstream applications. Human-Robot Interaction standards informed by empirical data could save us time and effort and provide us with the path toward the robots of the future. To this end, we share some of our pilot studies, lessons learned, and how they affected the outcome of our experiments. While these aspects might not be publishable in themselves, we hope our work might save time and effort for other researchers towards their research and serve as additional considerations for discussion at the workshop.
