标题： RoboCrowd：通过众包扩展机器人数据收集 显示英文标题

标题： RoboCrowd: Scaling Robot Data Collection through Crowdsourcing

摘要： 近年来，从大规模人类演示中进行模仿学习已成为训练机器人策略的一种有前景的范式。 然而，在收集大量人类演示方面，无论是收集时间还是获得专家操作员的访问权限，都存在显著负担。 我们引入了一种新的数据收集范式，RoboCrowd，它通过利用众包原则和激励设计来分担负载。 RoboCrowd有助于实现可扩展的数据收集，并促进更高效的机器人策略学习。 我们在ALOHA（Zhao等人，2023）之上构建了RoboCrowd——一个支持通过木偶操控进行数据收集的双臂平台——以探索在公共环境中众包现场演示的设计空间。 我们提出了三类激励机制，以吸引用户因不同动机而与系统互动：物质奖励、内在兴趣和社会比较。 我们通过包括物质奖励、引人入胜或具有挑战性的操作以及排行榜等游戏化元素的任务来实现这些激励。 我们在大学咖啡厅中进行了为期两周的大规模实地实验。 我们观察到用户对该系统的显著参与度——超过200名个人独立自愿提供总计超过800次交互会话。 我们的研究结果验证了所提出的激励机制作为塑造用户数据数量和质量的有效方式。 此外，我们证明了众包数据可以作为在专家演示上微调策略的有用预训练数据——与没有此数据相比，性能提升高达20%。 这些结果表明，通过精心实施众包和激励设计原则，RoboCrowd有可能减轻机器人数据收集的负担。 显示英文摘要

摘要： In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large quantities of human demonstrations is significant in terms of collection time and the need for access to expert operators. We introduce a new data collection paradigm, RoboCrowd, which distributes the workload by utilizing crowdsourcing principles and incentive design. RoboCrowd helps enable scalable data collection and facilitates more efficient learning of robot policies. We build RoboCrowd on top of ALOHA (Zhao et al. 2023) -- a bimanual platform that supports data collection via puppeteering -- to explore the design space for crowdsourcing in-person demonstrations in a public environment. We propose three classes of incentive mechanisms to appeal to users' varying sources of motivation for interacting with the system: material rewards, intrinsic interest, and social comparison. We instantiate these incentives through tasks that include physical rewards, engaging or challenging manipulations, as well as gamification elements such as a leaderboard. We conduct a large-scale, two-week field experiment in which the platform is situated in a university cafe. We observe significant engagement with the system -- over 200 individuals independently volunteered to provide a total of over 800 interaction episodes. Our findings validate the proposed incentives as mechanisms for shaping users' data quantity and quality. Further, we demonstrate that the crowdsourced data can serve as useful pre-training data for policies fine-tuned on expert demonstrations -- boosting performance up to 20% compared to when this data is not available. These results suggest the potential for RoboCrowd to reduce the burden of robot data collection by carefully implementing crowdsourcing and incentive design principles.