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

2025-03-11

arXiv.org

AI Summary

This study investigates how virtual reality (VR) versus traditional 2D interfaces affect the quality of human feedback and policy alignment in preference-based robot learning (PbRL). To this end, we construct the first cross-modal (VR/2D) human navigation preference dataset—comprising 2,325 pairwise comparisons—and integrate preference modeling, a custom VR experimental platform, and robot policy training to conduct systematic human-in-the-loop evaluation and statistical analysis. Results show that VR enhances spatial situational awareness but significantly reduces preference consistency; conversely, 2D interfaces yield more stable feedback yet impair semantic understanding of the environment. We are the first to empirically characterize the trade-offs among interface immersion, preference reliability, user consistency, and downstream policy performance. Furthermore, we publicly release the dual-modality dataset to serve as both an empirical foundation and benchmark resource for human–robot interface design in PbRL.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Learning Human Values and Preferences
• Knowledge Representation and Reasoning: Preferences

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Examining how VR versus 2D interfaces affect human preference collection
• Analyzing interface impact on preference consistency and navigation policy alignment
• Investigating trade-offs between immersion, perception, and preference reliability

Innovation

• VR and 2D interfaces for preference collection
• Dataset of 2,325 human preference queries
• Systematic comparison of interface modality impacts