訓練されたヒューマノイドロボットによる人間らしいクロスモーダル社会的注意と競合解決
A trained humanoid robot can perform human-like crossmodal social attention and conflict resolution
視線と音の位置が一致・不一致な円卓会議シーンで人間の注意応答を計測し、顕著性予測モデルをiCubに搭載して人間類似のクロスモーダル注意と競合解決を再現した研究。
著者: Di Fu, Fares Abawi, Hugo Carneiro, Matthias Kerzel, Ziwei Chen, Erik Strahl, Xun Liu, Stefan Wermter
分類: cs.RO, cs.AI, cs.HC
原文アブストラクト
To enhance human-robot social interaction, it is essential for robots to process multiple social cues in a complex real-world environment. However, incongruency of input information across modalities is inevitable and could be challenging for robots to process. To tackle this challenge, our study adopted the neurorobotic paradigm of crossmodal conflict resolution to make a robot express human-like social attention. A behavioural experiment was conducted on 37 participants for the human study. We designed a round-table meeting scenario with three animated avatars to improve ecological validity. Each avatar wore a medical mask to obscure the facial cues of the nose, mouth, and jaw. The central avatar shifted its eye gaze while the peripheral avatars generated sound. Gaze direction and sound locations were either spatially congruent or incongruent. We observed that the central avatar's dynamic gaze could trigger crossmodal social attention responses. In particular, human performances are better under the congruent audio-visual condition than the incongruent condition. Our saliency prediction model was trained to detect social cues, predict audio-visual saliency, and attend selectively for the robot study. After mounting the trained model on the iCub, the robot was exposed to laboratory conditions similar to the human experiment. While the human performances were overall superior, our trained model demonstrated that it could replicate attention responses similar to humans.
