NRI: Robotic Tool-Use for Cleaning

据报道，根据对潜在用户的调查和采访，清洁是个人机器人最想要的能力之一。然而，到目前为止，机器人清洁的成功解决方案是为特定的清洁任务设计的专用机器人，如吸尘器或拖把机器人。相反，该项目旨在通过使通用移动机械手能够使用人类工具来执行更广泛的清洁任务，包括除尘、擦拭、擦洗、扫地或拖地。清洁是日常生活中的一项重要活动，也是维持生活质量的一项重要任务。当许多老年人不能再执行清洁任务时，他们就失去了独立性。同样，许多行动不便的人依赖他人进行清洁。因此，承担这些任务的机器人可能会对数百万人产生积极影响。通用清洁机器人也可能产生重大的经济影响，类似于Roomba等特殊用途的机器人。在酒店或百货公司等许多商业设施中，清洁度是至关重要的，这些设施涉及大型表面，需要定期以本项目开发的可编程清洁机器人可以处理的特定方式进行清洁。通用清洁机器人需要能够根据许多不同的用户偏好，在许多不同的环境中使用多种不同的清洁工具。可能情况的多样性使开发用于任意清洁任务的通用控制器或计划器变得困难。此外，不可能事先知道用户的偏好。为了应对这些挑战，该项目开发了一种从演示中学习(LFD)的技术，允许机器人从人类演示中学习清洁动作。这项技术利用了清洁任务的常见结构：它们都涉及以特定图案相对于目标表面移动清洁工具，同时对表面施加一定的力。尽管已经存在许多LFD方法，但它们都不允许学习适用于具有任意清洁模式的任意表面的清洁操作。这个项目贡献了新的表示和学习算法，使移动机械手能够使用不同类型的清洁工具来清洁任意大小、形状、曲率、污垢分布和杂乱的表面。该项目开发的方法通过实验室的系统实验和用户研究，以及在现实世界中通过部署在私人家庭中的相同机器人，通过真实的清洁任务进行评估。

Cleaning is as one of the most desired capabilities for personal robots, according to reported surveys and interviews conducted with potential users. However, successful solutions to robotic cleaning have so far been special-purpose robots designed for particular cleaning tasks, as with the vacuuming or mopping robots. Instead, this project aims to make general-purpose mobile manipulators perform a much wider range of cleaning tasks, including dusting, wiping, scrubbing, sweeping, or mopping, by enabling them to use human tools. Cleaning is a key activity of daily living and an important task in the maintenance of one's quality of life. Many older adults lose their independence when they can no longer carry out cleaning tasks. Similarly, many mobility-impaired individuals rely on others for cleaning. Robots taking on these tasks could therefore positively impact millions of individuals. General-purpose cleaning robots could also have significant economic impact, similar to those of special-purpose ones like the Roomba. Cleanliness is critical in many commercial facilities, such as hotels or department stores, which involve large surfaces that need to be regularly cleaned in specific ways that programmable cleaning robots developed in this project could handle.General-purpose cleaning robots need to be able to use many different cleaning tools, in many different environments, based on many different user preferences. The diversity of possible situations makes it difficult to develop universal controllers or planners for arbitrary cleaning tasks. Furthermore, it is impossible to know the users' preferences in advance. To address these challenges, this project develops a Learning from Demonstration (LfD) technique that allows a robot to learn cleaning actions from human demonstrations. This technique exploits the common structure of cleaning tasks: they all involve moving a cleaning tool relative to a target surface with a certain pattern while applying a certain force on the surface. Although many LfD approaches already exist, none of them allow learning cleaning actions that generalize to arbitrary surfaces with arbitrary cleaning patterns. This project contributes new representations and learning algorithms that enables a mobile manipulator to clean surfaces of arbitrary size, shape, curvature, dirt distribution, and clutter, using different types of cleaning tools. The methods developed in the project are evaluated with real cleaning tasks through systematic experiments and user studies in the lab, as well as, in the real world through an identical robot deployed in a private home.