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.

翻译：在本研究中，我们旨在通过开发适用于不同学习能力群体的机器教学算法，提升人机协作的透明度与效率。以往的方法侧重于针对个体的定制化教学策略，而我们的方法则利用团队信念表示，针对由不同能力学习者组成的各类团队进行教学，以应对群体内的个性化挑战。我们探讨了多种群体教学策略，例如聚焦于个体信念或群体集体信念，并评估了它们对不同团队组成下机器人策略学习效果的影响。研究结果表明，与个体信念策略相比，团队信念策略能带来更稳定的学习时长，并更好地适应多样化团队，从而表明其在资源有限的混合能力场景中更具适用性。相反，个体信念策略能提供更均匀的知识水平，尤其适用于同质化且经验不足的群体。本研究表明，教学策略的效果显著受团队组成与学习者能力的影响，这凸显了实时评估学习者能力、并基于能力调整教学方法以实现最优教学效果的重要性。