@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}},
}

@article{61327,
  abstract     = {{Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or
adapt existing ones to new situations. Recent advances in artificial intelligence, including learning approaches like reinforcement
learning and architectures like transformers and foundation models, combined with access to massive datasets, has created attractive
opportunities to apply those data-hungry techniques to this problem. We argue that the focus on massive amounts of pre-collected
data, and the resulting learning paradigm, where humans demonstrate and robots learn in isolation, is overshadowing a specialized
area of work we term Human-Interactive-Robot-Learning (HIRL). This paradigm, wherein robots and humans interact during the
learning process, is at the intersection of multiple fields (artificial intelligence, robotics, human-computer interaction, design and others)
and holds unique promise. Using HIRL, robots can achieve greater sample efficiency (as humans can provide task knowledge through
interaction), align with human preferences (as humans can guide the robot behavior towards their expectations), and explore more
meaningfully and safely (as humans can utilize domain knowledge to guide learning and prevent catastrophic failures). This can result
in robotic systems that can more quickly and easily adapt to new tasks in human environments. The objective of this paper is to
provide a broad and consistent overview of HIRL research and to guide researchers toward understanding the scope of HIRL, and
current open or underexplored challenges related to four themes — namely, human, robot learning, interaction, and broader context.
The paper includes concrete use cases to illustrate the interaction between these challenges and inspire further research according to
broad recommendations and a call for action for the growing HIRL community}},
  author       = {{Baraka, Kim  and Idrees, Ifrah and Faulkner, Taylor Kessler and Biyik, Erdem and Booth, Serena and Chetouani, Mohamed and Grollman, Daniel H. and Saran, Akanksha and Senft, Emmanuel and Tulli, Silvia and Vollmer, Anna-Lisa and Andriella, Antonio and Beierling, Helen and Horter, Tiffany and Kober, Jens and Sheidlower, Isaac and Taylor, Matthew E. and van Waveren, Sanne and Xiao, Xuesu}},
  journal      = {{Transactions on Human-Robot Interaction}},
  keywords     = {{Robot learning, Interactive learning systems, Human-robot interaction, Human-in-the-loop machine learning, Teaching and learning}},
  title        = {{{Human-Interactive Robot Learning: Definition, Challenges, and Recommendations}}},
  year         = {{2025}},
}