MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze
MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze
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DOI：
10.1109/lra.2020.3043167
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发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Mainprice, Jim
中科院分区：
文献类型：
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作者：
Kratzer, Philipp;Bihlmaier, Simon;Mainprice, Jim
随着机器人越来越多地出现在开放的人类环境中，机器人系统理解和预测人类运动将变得至关重要。这种能力在很大程度上取决于运动捕捉数据的质量和可用性。然而，现有的全身运动数据集很少包括1）长序列的操纵任务，2）工作空间几何形状的3D模型，以及3）眼睛注视，当机器人需要预测人类的运动时，这些都很重要。因此，在这封信中，我们提出了一个新的全身运动数据集，用于日常操作任务，其中包括上述内容。运动数据的捕获使用传统的运动捕捉系统的基础上反射标记。此外，我们还使用可穿戴的瞳孔跟踪设备捕获了眼睛注视。正如我们在实验中所示，该数据集可用于全身运动预测算法的设计和评估。此外，我们的实验表明，眼睛凝视是人类意图的一个强有力的预测因素。该数据集包括180分钟的动作捕捉数据，其中执行了1627个拾取和放置动作。它可以在https://humans-to-robots-motion.github.io/mogaze/ MoGaze，Dataset上找到，并计划在不久的将来扩展到两个人的协作任务。
As robots become more present in open human environments, it will become crucial for robotic systems to understand and predict human motion. Such capabilities depend heavily on the quality and availability of motion capture data. However, existing datasets of full-body motion rarely include 1) long sequences of manipulation tasks, 2) the 3D model of the workspace geometry, and 3) eye-gaze, which are all important when a robot needs to predict the movements of humans in close proximity. Hence, in this letter, we present a novel dataset of full-body motion for everyday manipulation tasks, which includes the above. The motion data was captured using a traditional motion capture system based on reflective markers. We additionally captured eye-gaze using a wearable pupil-tracking device. As we show in experiments, the dataset can be used for the design and evaluation of full-body motion prediction algorithms. Furthermore, our experiments show eye-gaze as a powerful predictor of human intent. The dataset includes 180 min of motion capture data with 1627 pick and place actions being performed. It is available at https://humans-to-robots-motion.github.io/mogaze/ MoGaze, Dataset and is planned to be extended to collaborative tasks with two humans in the near future.