The Impact of {VR} and 2D Interfaces on Human Feedback in Preference-Based Robot Learning

Aligning robot navigation with human preferences is essential for ensuring comfortable and predictable robot movement in shared spaces, facilitating seamless human-robot coexistence. While preference-based learning methods, such as reinforcement learning from human feedback ({RLHF}), enable this alignment, the choice of the preference collection interface may influence the process. Traditional 2D interfaces provide structured views but lack spatial depth, whereas immersive {VR} offers richer perception, potentially affecting preference articulation. This study systematically examines how the interface modality impacts human preference collection and navigation policy alignment. We introduce a novel dataset of 2,325 human preference queries collected through both {VR} and 2D interfaces, revealing significant differences in user experience, preference consistency, and policy outcomes. Our findings highlight the trade-offs between immersion, perception, and preference reliability, emphasizing the importance of interface selection in preference-based robot learning. The dataset will be publicly released to support future research.