This study contributes to the evolving field of robot learning in interaction with humans, examining the impact of diverse input modalities on learning outcomes. It introduces the concept of "meta-modalities" which encapsulate additional forms of feedback beyond the traditional preference and scalar feedback mechanisms. Unlike prior research that focused on individual meta-modalities, this work evaluates their combined effect on learning outcomes. Through a study with human participants, we explore user preferences for these modalities and their impact on robot learning performance. Our findings reveal that while individual modalities are perceived differently, their combination significantly improves learning behavior and usability. This research not only provides valuable insights into the optimization of human-robot interactive task learning but also opens new avenues for enhancing the interactive freedom and scaffolding capabilities provided to users in such settings.

翻译：本研究致力于人机交互中机器人学习这一不断发展领域，探讨不同输入模态对学习效果的影响。我们提出了“元模态”概念，这些元模态涵盖传统偏好与标量反馈机制之外的附加反馈形式。与以往关注单一元模态的研究不同，本工作评估了元模态组合对学习效果的综合影响。通过一项人类参与者实验，我们探索了用户对这些模态的偏好及其对机器人学习性能的影响。研究结果揭示，虽然不同模态的感知存在差异，但其组合显著提升了学习行为与可用性。本项研究不仅为优化人机交互式任务学习提供了宝贵见解，还开辟了增强此类场景中用户交互自由度与支架能力的新途径。