The Power of Combined Modalities in Interactive Robot Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 13 May 2024.
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6 of 23 references.
References · 6
- 2023A Survey of Reinforcement Learning from Human Feedbackcited by 264
- 2018DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedbackcited by 41
- 2021A Survey on Interactive Reinforcement Learning: Design Principles and Open Challengescited by 41
- 2023Interactive Robot Learning from Verbal Correctioncited by 29
- 2021Learning Human Objectives from Sequences of Physical Correctionscited by 21
- 2023Learning a Universal Human Prior for Dexterous Manipulation from Human Preferencecited by 10
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