标题： 探索基于上下文感知和大型语言模型驱动的沉浸式虚拟现实运动方式 显示英文标题

标题： Exploring Context-aware and LLM-driven Locomotion for Immersive Virtual Reality

摘要： 运动在塑造虚拟现实环境中的用户体验方面起着至关重要的作用。特别是，免手持运动提供了一种有价值的替代方案，通过支持可访问性并让用户摆脱对手持控制器的依赖。然而，传统的基于语音的方法通常依赖于严格的命令集，限制了交互的自然性和灵活性。在这项研究中，我们提出了一种由大型语言模型（LLMs）驱动的新颖运动技术，使用户能够使用具有上下文感知的自然语言在虚拟环境中导航。我们评估了三种运动方法：基于控制器的瞬移、基于语音的方向控制以及我们的基于语言模型的方法。我们的评估指标包括眼动数据分析，包括通过SHAP分析的可解释机器学习，以及标准化问卷调查以衡量可用性、临场感、晕动症和认知负荷，以检查用户的注意力和参与度。我们的研究结果表明，由LLM驱动的运动在可用性、临场感和晕动症评分上与现有的方法（如瞬移）相当，展示了其作为舒适、基于自然语言、免手持替代方案的潜力。此外，它增强了用户在虚拟环境中的注意力，表明更高的参与度。此外，SHAP分析显示，注视、扫视和瞳孔相关特征在不同技术之间存在差异，表明视觉注意和认知处理的不同模式。总体而言，我们认为我们的方法可以促进虚拟空间中的免手持运动，特别是在支持可访问性方面。 显示英文摘要

摘要： Locomotion plays a crucial role in shaping the user experience within virtual reality environments. In particular, hands-free locomotion offers a valuable alternative by supporting accessibility and freeing users from reliance on handheld controllers. To this end, traditional speech-based methods often depend on rigid command sets, limiting the naturalness and flexibility of interaction. In this study, we propose a novel locomotion technique powered by large language models (LLMs), which allows users to navigate virtual environments using natural language with contextual awareness. We evaluate three locomotion methods: controller-based teleportation, voice-based steering, and our language model-driven approach. Our evaluation measures include eye-tracking data analysis, including explainable machine learning through SHAP analysis as well as standardized questionnaires for usability, presence, cybersickness, and cognitive load to examine user attention and engagement. Our findings indicate that the LLM-driven locomotion possesses comparable usability, presence, and cybersickness scores to established methods like teleportation, demonstrating its novel potential as a comfortable, natural language-based, hands-free alternative. In addition, it enhances user attention within the virtual environment, suggesting greater engagement. Complementary to these findings, SHAP analysis revealed that fixation, saccade, and pupil-related features vary across techniques, indicating distinct patterns of visual attention and cognitive processing. Overall, we state that our method can facilitate hands-free locomotion in virtual spaces, especially in supporting accessibility.