VRと2Dインターフェースが選好に基づくロボット学習における人間のフィードバックに与える影響
The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning
ロボットナビゲーションの選好学習において、VRと2Dインターフェースのどちらで人間のフィードバックを集めるかが、ユーザー体験や選好の一貫性、学習された方策に与える影響を大規模データセットで比較分析した研究。
著者: Jorge de Heuvel, Daniel Marta, Simon Holk, Iolanda Leite, Maren Bennewitz
分類: cs.HC, cs.RO
原文アブストラクト
Aligning robot navigation with human preferences is essential for ensuring comfortable, and predictable robot movement in shared spaces. 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 is available to support future research.
