Huang Yongqiang, Bianchi Matteo, Liarokapis Minas, Sun Yu
1 Department of Computer Science and Engineering, University of South Florida , Tampa, Florida.
2 Research Center "E. Piaggio," Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy .
Big Data. 2016 Dec;4(4):197-216. doi: 10.1089/big.2016.0042.
Data sets is crucial not only for model learning and evaluation but also to advance knowledge on human behavior, thus fostering mutual inspiration between neuroscience and robotics. However, choosing the right data set to use or creating a new data set is not an easy task, because of the variety of data that can be found in the related literature. The first step to tackle this issue is to collect and organize those that are available. In this work, we take a significant step forward by reviewing data sets that were published in the past 10 years and that are directly related to object manipulation and grasping. We report on modalities, activities, and annotations for each individual data set and we discuss our view on its use for object manipulation. We also compare the data sets and summarize them. Finally, we conclude the survey by providing suggestions and discussing the best practices for the creation of new data sets.
数据集不仅对模型学习和评估至关重要，而且对于增进对人类行为的认识也很关键，从而促进神经科学与机器人技术之间的相互启发。然而，选择合适的数据集来使用或创建新的数据集并非易事，因为在相关文献中可以找到各种各样的数据。解决这个问题的第一步是收集和整理现有的数据集。在这项工作中，我们通过回顾过去10年发表的、与物体操纵和抓取直接相关的数据集向前迈出了重要一步。我们报告了每个单独数据集的模态、活动和注释，并讨论了我们对其在物体操纵方面用途的看法。我们还对这些数据集进行了比较和总结。最后，我们通过提供建议和讨论创建新数据集的最佳实践来结束这项调查。
