In recent years, we have seen an emergence of data-driven approaches in
robotics. However, most existing efforts and datasets are either in simulation
or focus on a single task in isolation such as grasping, pushing or poking. In
order to make progress and capture the space of manipulation, we would need to
collect a large-scale dataset of diverse tasks such as pouring, opening
bottles, stacking objects etc. But how does one collect such a dataset? In this
paper, we present the largest available robotic-demonstration dataset (MIME)
that contains 8260 human-robot demonstrations over 20 different robotic tasks
(this https URL). These tasks range from the simple
task of pushing objects to the difficult task of stacking household objects.
Our dataset consists of videos of human demonstrations and kinesthetic
trajectories of robot demonstrations. We also propose to use this dataset for
the task of mapping 3rd person video features to robot trajectories.
Furthermore, we present two different approaches using this dataset and
evaluate the predicted robot trajectories against ground-truth trajectories. We
hope our dataset inspires research in multiple areas including visual
imitation, trajectory prediction, and multi-task robotic learning.

该论文介绍了当前机器人技术中数据驱动方法的应用以及面临的困难，提出了收集人 - 机器人大规模教学数据集的必要性和如何收集，同时介绍了 MIME 数据集和利用该数据集进行轨迹预测和多任务学习和其它相关研究的应用案例。