The power of combined modalities in interactive robot learning

With the continuous advancement of Artificial intelligence (AI), robots as embodied intelligent systems are increasingly becoming more present in daily life like households or in elderly care. As a result, lay users are required to interact with these systems more frequently and teach them to meet individual needs. Human-in-the-loop reinforcement learning (HIL-RL) offers an effective way to realize this teaching. Studies show that various feedback modalities, such as preference, guidance, or demonstration can significantly enhance learning success, though their suitability varies among users expertise in robotics. Research also indicates that users apply different scaffolding strategies when teaching a robot, such as motivating it to explore actions that promise success. Thus, providing a collection of different feedback modalities allows users to choose the method that best suits their teaching strategy, and allows the system to individually support the user based on their interaction behavior. However, most state-of-the-art approaches provide users with only one feedback modality at a time. Investigating combined feedback modalities in interactive robot learning remains an open challenge. To address this, we conducted a study that combined common feedback modalities. Our research questions focused on whether these combinations improve learning outcomes, reveal user preferences, show differences in perceived effectiveness, and identify which modalities influence learning the most. The results show that combining the feedback modalities improves learning, with users perceiving the effectiveness of the modalities vary ways, and certain modalities directly impacting learning success. The study demonstrates that combining feedback modalities can support learning even in a simplified setting and suggests the potential for broader applicability, especially in robot learning scenarios with a focus on user interaction. Thus, this paper aims to motivate the use of combined feedback modalities in interactive imitation learning.