@article{55400,
  abstract     = {{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.}},
  author       = {{Beierling, Helen and Beierling, Robin  and Vollmer, Anna-Lisa}},
  journal      = {{Frontiers in Robotics and AI}},
  keywords     = {{human-robot interaction, human-in-the-loop learning, reinforcement learning, interactive robot learning, multi-modal feedback, learning from demonstration, preference-based learning, scaffolding in robot learning}},
  publisher    = {{Frontiers }},
  title        = {{{The power of combined modalities in interactive robot learning}}},
  volume       = {{12}},
  year         = {{2025}},
}