ロボットの心を理解する：多様なグループ間での透明な人間-ロボット協働のための機械教育の活用
Understanding Robot Minds: Leveraging Machine Teaching for Transparent Human-Robot Collaboration Across Diverse Groups
多様な学習能力を持つグループに対して、チーム信念表現を用いた機械教育アルゴリズムを開発し、グループ構成や学習者の熟練度に応じた教育戦略の有効性を調査した。
著者: Suresh Kumaar Jayaraman, Reid Simmons, Aaron Steinfeld, Henny Admoni
分類: cs.RO
原文アブストラクト
In this work, we aim to improve transparency and efficacy in human-robot collaboration by developing machine teaching algorithms suitable for groups with varied learning capabilities. While previous approaches focused on tailored approaches for teaching individuals, our method teaches teams with various compositions of diverse learners using team belief representations to address personalization challenges within groups. We investigate various group teaching strategies, such as focusing on individual beliefs or the group's collective beliefs, and assess their impact on learning robot policies for different team compositions. Our findings reveal that team belief strategies yield less variation in learning duration and better accommodate diverse teams compared to individual belief strategies, suggesting their suitability in mixed-proficiency settings with limited resources. Conversely, individual belief strategies provide a more uniform knowledge level, particularly effective for homogeneously inexperienced groups. Our study indicates that the teaching strategy's efficacy is significantly influenced by team composition and learner proficiency, highlighting the importance of real-time assessment of learner proficiency and adapting teaching approaches based on learner proficiency for optimal teaching outcomes.
