RoSHI：一种面向机器人的多功能服装
RoSHI: A Versatile Robot-oriented Suit for Human Data In-the-Wild
AI总结：
RoSHI结合低成本稀疏IMU与Project Aria眼镜，实现从第一人称感知中估计完整3D姿态和身体形状，用于真实世界机器人学习。
摘要：
扩大机器人学习规模可能需要包含丰富且长周期交互的真实人类数据。现有数据收集方法在可携带性、遮挡鲁棒性和全局一致性之间权衡。我们引入RoSHI，一种混合可穿戴设备，融合低成本稀疏IMU与Project Aria眼镜，从第一人称感知中估计佩戴者的完整3D姿态和身体形状，以度量全局坐标系。该系统受两种传感器互补性的启发：IMU提供遮挡鲁棒性和高速运动能力，而第一人称SLAM锚定长周期运动并稳定上半身姿态。我们收集了敏捷活动数据集来评估RoSHI。在该数据集上，我们通常优于其他第一人称基线，并与最先进的外视基线（SAM3D）表现相当。最后，我们证明了该系统记录的运动数据适用于真实世界人形政策学习。对于视频、数据和更多内容，请访问项目网页：https://roshi-mocap.github.io/
英文摘要：
Scaling up robot learning will likely require human data containing rich and long-horizon interactions in the wild. Existing approaches for collecting such data trade off portability, robustness to occlusion, and global consistency. We introduce RoSHI, a hybrid wearable that fuses low-cost sparse IMUs with the Project Aria glasses to estimate the full 3D pose and body shape of the wearer in a metric global coordinate frame from egocentric perception. This system is motivated by the complementarity of the two sensors: IMUs provide robustness to occlusions and high-speed motions, while egocentric SLAM anchors long-horizon motion and stabilizes upper body pose. We collect a dataset of agile activities to evaluate RoSHI. On this dataset, we generally outperform other egocentric baselines and perform comparably to a state-of-the-art exocentric baseline (SAM3D). Finally, we demonstrate that the motion data recorded from our system are suitable for real-world humanoid policy learning. For videos, data and more, visit the project webpage: https://roshi-mocap.github.io/
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。
