论文标题
ActionEt：3D环境中基于任务的数据收集和增强的交互式端到端平台
Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
论文作者
论文摘要
人造代理的任务计划的问题在很大程度上尚未解决。尽管对数据驱动的方法进行人工代理的任务计划的兴趣越来越多，但剩下的瓶颈是大规模全面的基于任务的数据集的缺乏。在本文中，我们提出了Actionet，这是一个交互式的端到端平台，用于数据收集和增强3D环境中的基于任务的数据集。使用Actionet，我们收集了一个大规模的综合基于任务的数据集，其中包括3000多个层次结构结构和视频。使用层次任务结构，在50个不同场景中进一步增强了视频，以提供超过150,000个视频。据我们所知，Actionet是此类基于任务数据集生成的第一个交互式端到端平台，而随附的数据集是这种全面性质的最大基于任务的数据集。 Actionet平台和数据集将用于促进层次任务计划中的研究。
The problem of task planning for artificial agents remains largely unsolved. While there has been increasing interest in data-driven approaches for the study of task planning for artificial agents, a significant remaining bottleneck is the dearth of large-scale comprehensive task-based datasets. In this paper, we present ActioNet, an interactive end-to-end platform for data collection and augmentation of task-based dataset in 3D environment. Using ActioNet, we collected a large-scale comprehensive task-based dataset, comprising over 3000 hierarchical task structures and videos. Using the hierarchical task structures, the videos are further augmented across 50 different scenes to give over 150,000 video. To our knowledge, ActioNet is the first interactive end-to-end platform for such task-based dataset generation and the accompanying dataset is the largest task-based dataset of such comprehensive nature. The ActioNet platform and dataset will be made available to facilitate research in hierarchical task planning.
