メタ強化学習による高速適応と人間-ロボット間の信頼モデリング
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction
メタ学習ベースの方策勾配法で人間-ロボット相互作用の適応問題を解決し、混合現実の脱出ゲームで双方向の信頼に与える影響を検証した。提案手法はロボットの信頼性知覚を高め、人間の信頼獲得のダイナミクスに影響を与えた。
著者: Yuan Gao, Elena Sibirtseva, Ginevra Castellano, Danica Kragic
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.
