穿行于杂乱之中：通过虚拟现实实现3D场景感知人类oid运动的 数据收集与基准测试
Moving Through Clutter: Scaling Data Collection and Benchmarking for 3D Scene-Aware Humanoid Locomotion via Virtual Reality
AI总结：
MTC通过虚拟现实框架收集和评估3D场景感知的人oid运动数据，提供348条轨迹的145种杂乱场景数据集，用于研究几何适应性和开发场景感知的规划与控制方法。
摘要：
最近的人oid运动进展使动态行为如跳舞、武术和公园跑得以实现，但这些能力主要在开放、平坦和无障碍的环境中展示。相比之下，现实世界中的环境如家庭、办公室和公共场所密集杂乱、三维且几何受限，需要场景感知的全身协调、精确的平衡控制以及对家具和日常物品所施加的空间约束的推理。然而，人类oid在杂乱3D环境中的运动仍处于探索阶段，且没有公开的数据集系统地将全身人类运动与塑造其的场景几何结合起来。为了解决这一差距，我们提出了Moving Through Clutter（MTC），一个开源的基于虚拟现实（VR）的数据收集和评估框架，用于在杂乱环境中实现场景感知的人oid运动。我们的系统通过可控的杂乱水平生成场景，并通过沉浸式VR导航捕获一致的全身人类运动，然后自动将其重新定向到人形机器人模型。我们进一步引入了基准测试，量化环境杂乱程度和运动性能，包括稳定性和碰撞安全性。使用该框架，我们整理了348条轨迹，覆盖145种不同的3D杂乱场景。该数据集为研究几何诱导的适应性在人形运动中的应用以及开发场景感知的规划和控制方法提供了基础。
英文摘要：
Recent advances in humanoid locomotion have enabled dynamic behaviors such as dancing, martial arts, and parkour, yet these capabilities are predominantly demonstrated in open, flat, and obstacle-free settings. In contrast, real-world environments such as homes, offices, and public spaces, are densely cluttered, three-dimensional, and geometrically constrained, requiring scene-aware whole-body coordination, precise balance control, and reasoning over spatial constraints imposed by furniture and household objects. However, humanoid locomotion in cluttered 3D environments remains underexplored, and no public dataset systematically couples full-body human locomotion with the scene geometry that shapes it. To address this gap, we present Moving Through Clutter (MTC), an opensource Virtual Reality (VR) based data collection and evaluation framework for scene-aware humanoid locomotion in cluttered environments. Our system procedurally generates scenes with controllable clutter levels and captures embodiment-consistent, whole-body human motion through immersive VR navigation, which is then automatically retargeted to a humanoid robot model. We further introduce benchmarks that quantify environment clutter level and locomotion performance, including stability and collision safety. Using this framework, we compile a dataset of 348 trajectories across 145 diverse 3D cluttered scenes. The dataset provides a foundation for studying geometry-induced adaptation in humanoid locomotion and developing scene-aware planning and control methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
