複数タスク間における人間のロボット信頼の転移モデル
Multi-Task Trust Transfer for Human-Robot Interaction
家庭内作業と自動運転の2タスクで人間の信頼形成を調査し、潜在タスク表現を用いた信頼の進化・転移モデル（ベイズ・ニューラル・ハイブリッド）を提案。未知タスクや未知ユーザーへの信頼予測で既存手法を上回った。
著者: Harold Soh, Yaqi Xie, Min Chen, David Hsu
分類: cs.RO, cs.HC
原文アブストラクト
Trust is essential in shaping human interactions with one another and with robots. This paper discusses how human trust in robot capabilities transfers across multiple tasks. We first present a human-subject study of two distinct task domains: a Fetch robot performing household tasks and a virtual reality simulation of an autonomous vehicle performing driving and parking maneuvers. The findings expand our understanding of trust and inspire new differentiable models of trust evolution and transfer via latent task representations: (i) a rational Bayes model, (ii) a data-driven neural network model, and (iii) a hybrid model that combines the two. Experiments show that the proposed models outperform prevailing models when predicting trust over unseen tasks and users. These results suggest that (i) task-dependent functional trust models capture human trust in robot capabilities more accurately, and (ii) trust transfer across tasks can be inferred to a good degree. The latter enables trust-mediated robot decision-making for fluent human-robot interaction in multi-task settings.
