Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
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DOI：
10.15607/rss.2022.xviii.063
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
F. Ebert;Yanlai Yang;Karl Schmeckpeper;Bernadette Bucher;G. Georgakis;Kostas Daniilidis;Chelsea Finn;S. Levine
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文献类型：
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作者：
F. Ebert;Yanlai Yang;Karl Schmeckpeper;Bernadette Bucher;G. Georgakis;Kostas Daniilidis;Chelsea Finn;S. Levine
机器人学习有望实现广泛泛化的学习策略。然而，这种泛化需要对感兴趣的任务进行足够多样化的数据集，而收集这些数据集的成本可能非常高。在其他领域，如计算机视觉，通常利用共享的、可重用的数据集（如ImageNet）来克服这一挑战，但这在机器人技术中已被证明是困难的。在本文中，我们提出了这样的问题：在机器人技术中实现端到端技能学习的实际数据重用需要什么？我们假设关键是使用具有多个任务和多个领域的数据集，这样想要训练机器人在新领域执行新任务的新用户可以将该数据集包含在他们的训练过程中，并从跨任务和跨领域泛化中受益。为了评估这一假设，我们收集了一个大型的多领域和多任务数据集，其中包含7200个演示，构成了10个环境中的71个任务，并实证研究了这些数据如何改善新环境中新任务的学习。我们发现，与单独使用目标领域数据相比，使用提议的数据集和在新领域中进行50次从未见过的任务演示的联合训练平均可使成功率提高2倍。我们还发现，在一个新领域中，只有少数任务的数据可以弥合领域差距，并使机器人有可能执行只在其他领域中看到的各种先前任务。这些结果表明，重用不同的多任务和多领域数据集，包括我们的开源数据集，可能为更广泛的机器人泛化铺平道路，消除了为每个新的机器人学习项目重新收集数据的需要。
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.