視線の駆け引き：ロボットの視線戦略が人間の協力行動に与える影響
Judging by the Look: The Impact of Robot Gaze Strategies on Human Cooperation
人間の視線データで学習した神経モデルを搭載したロボットが、協力ゲーム中に異なる視線戦略をとることで人間の行動がどう変わるかを比較検証した研究。
著者: Di Fu, Fares Abawi, Erik Strahl, Stefan Wermter
分類: cs.RO, cs.HC
原文アブストラクト
Human eye gaze plays an important role in delivering information, communicating intent, and understanding others' mental states. Previous research shows that a robot's gaze can also affect humans' decision-making and strategy during an interaction. However, limited studies have trained humanoid robots on gaze-based data in human-robot interaction scenarios. Considering gaze impacts the naturalness of social exchanges and alters the decision process of an observer, it should be regarded as a crucial component in human-robot interaction. To investigate the impact of robot gaze on humans, we propose an embodied neural model for performing human-like gaze shifts. This is achieved by extending a social attention model and training it on eye-tracking data, collected by watching humans playing a game. We will compare human behavioral performances in the presence of a robot adopting different gaze strategies in a human-human cooperation game.
