Robot learning holds the promise of learning policies that generalize
broadly. However, such generalization requires sufficiently diverse datasets of
the task of interest, which can be prohibitively expensive to collect. In other
fields, such as computer vision, it is common to utilize shared, reusable
datasets, such as ImageNet, to overcome this challenge, but this has proven
difficult in robotics. In this paper, we ask: what would it take to enable
practical data reuse in robotics for end-to-end skill learning? We hypothesize
that the key is to use datasets with multiple tasks and multiple domains, such
that a new user that wants to train their robot to perform a new task in a new
domain can include this dataset in their training process and benefit from
cross-task and cross-domain generalization. To evaluate this hypothesis, we
collect a large multi-domain and multi-task dataset, with 7,200 demonstrations
constituting 71 tasks across 10 environments, and empirically study how this
data can improve the learning of new tasks in new environments. We find that
jointly training with the proposed dataset and 50 demonstrations of a
never-before-seen task in a new domain on average leads to a 2x improvement in
success rate compared to using target domain data alone. We also find that data
for only a few tasks in a new domain can bridge the domain gap and make it
possible for a robot to perform a variety of prior tasks that were only seen in
other domains. These results suggest that reusing diverse multi-task and
multi-domain datasets, including our open-source dataset, may pave the way for
broader robot generalization, eliminating the need to re-collect data for each
new robot learning project.

本文旨在探索在机器人技能学习中实现实用的数据重用，通过使用包含多个任务和多个领域的多样化数据集进行训练，研究表明这种方法可以大大提高机器人在未曾见过的领域和任务中的成功率，大大降低了对采集昂贵的新数据集的需求。