外观
论文arXiv 2603.059937 个月前
MTC框架发布：VR采集348条杂乱场景人形机器人运动数据
本文由 AI 根据公开内容整理，可能有误，请以原文为准：Moving Through Clutter: Scaling Data Collection and Benchmarking for 3D Scene-Aware Humanoid Locomotion via Virtual Reality（arXiv 2603.05993）
核心内容
针对人形机器人在家庭等密集杂乱环境中缺乏全身体感与场景几何耦合数据的现状，研究团队发布开源框架Moving Through Clutter（MTC）。该系统利用VR沉浸式导航捕获符合具身约束的全身人类运动，并自动重定向至人形机器人模型，构建了包含145个多样化3D杂乱场景、348条高质量轨迹的专用数据集，填补了非结构化环境运动基准空白。
MTC支持程序化生成可控杂乱度的三维场景，解决了真实数据采集难问题。框架引入量化指标评估环境拥挤度及机器人运动性能，涵盖稳定性与碰撞安全性测试。通过将人类在受限空间中的避障与平衡策略映射到机器人本体，该数据集为研究几何诱导的运动适应机制、开发场景感知规划与控制算法提供了标准化的训练与评测基础。
关键看点
- VR数据采集：利用沉浸式VR导航捕获具身一致的全身人类运动，并自动重定向至人形机器人模型，解决真机采集成本高难题
- 基准数据集：开源包含145个多样化3D杂乱场景及348条轨迹的数据集，首次系统耦合全身体感运动与环境几何约束
- 场景生成：支持程序化生成三维场景并精确控制clutter水平，为研究几何诱导的运动适应提供标准化变量环境
- 量化评测：建立环境杂乱度与运动性能双重基准，涵盖稳定性及碰撞安全指标，支撑场景感知规划算法验证
工程与产业观察
MTC将人形机器人运动研究从开阔平地推向真实杂乱场景，通过VR低成本获取高价值几何约束数据。其“人类演示-自动重定向”范式有效缓解了具身数据稀缺瓶颈，量化基准的确立使非结构化环境下的全身协调控制具备了可复现的工程验证标准，加速Sim-to-Real落地。
英文摘要 (Abstract)
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.
作者：Beichen Wang, Yuanjie Lu, Linji Wang, Liuchuan Yu, Xuesu Xiao
来源
本页事实对应的公开页面，以原文为准。