Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
This addresses a bottleneck for researchers in hierarchical task planning by providing a novel dataset and tool, though it is incremental as it builds on data-driven approaches.
The paper tackles the lack of large-scale task-based datasets for AI task planning by introducing ActioNet, an interactive platform that collected over 3000 hierarchical task structures and videos, augmented to over 150,000 videos across 50 scenes.
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.