比较虚拟现实与二维界面如何影响人类对机器人导航的偏好反馈
The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning
- 用虚拟现实和二维界面收集2325条人类偏好数据
- VR界面虽沉浸但偏好一致性低于二维界面
- 界面选择直接影响机器人行为学习效果，适合人机交互研究者
让机器人导航符合人类偏好对共享空间中的舒适性和可预测性至关重要。尽管基于偏好的学习方法（如从人类反馈中强化学习，RLHF）能实现这一目标，但偏好收集界面的选择可能影响整个过程。传统二维界面提供结构化视图但缺乏空间深度，而沉浸式虚拟现实（VR）则提供更丰富的感知体验，可能影响偏好的表达。本研究系统考察了界面模态对人类偏好收集及导航策略对齐的影响。我们构建了一个包含2,325条人类偏好查询的新数据集，分别通过VR和二维界面采集，揭示了用户体感、偏好一致性及策略结果上的显著差异。研究发现沉浸感、感知能力与偏好可靠性之间存在权衡，强调了在基于偏好的机器人学习中界面选择的重要性。该数据集已公开，以支持未来研究。
原文摘要 · Abstract (English)
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.
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