Advancing robots with greater dynamic dexterity: A large-scale multi-view and multi-modal dataset of human-human throw&catch of arbitrary objects

Abstract

Learning and imitating behavioral intelligence from human demonstrations is a promising approach towards the intuitive programming of robots for enhanced dynamic dexterity. However, there has been no publicly available dataset in this domain. To address this gap, we introduce the first large-scale dataset and recording framework specifically designed for studying human collaborative dynamic dexterity in throw&catch tasks. The dataset, named H²TC, contains 15,000 multi-view and multi-modal synchronized recordings of diverse Human-Human Throw-and-Catch activities. It involves 34 human subjects with typical motor abilities and a variety of 52 objects frequently manipulated through throw&catch in domestic and/or industrial scenarios. The dataset is supplemented with a hierarchy of manually annotated semantic and dense labels, such as the ground truth human body, hand and object motions captured with specialized high-precision motion tracking systems. These rich annotations make the dataset well-suited for a wide range of robot studies, including both low-level motor skill learning and high-level cognitive planning and recognition. We envision that the proposed dataset and recording framework will facilitate learning pipelines to extract insights on how humans coordinate both intra- and interpersonally to throw and catch objects, ultimately leading to the development of more capable and collaborative robots. The dataset, along with a suite of utility tools, such as those for visualization and annotation, can be accessed from our project page at https://h2tc-roboticsx.github.io/.