NRI: Robotic Tool-Use for Cleaning

根据对潜在用户进行的调查和采访，清洁是个人机器人最期望的功能之一。然而，迄今为止，成功的机器人清洁解决方案是为特定清洁任务设计的专用机器人，如吸尘或拖地机器人。相反，该项目旨在使通用移动的机械手执行更广泛的清洁任务，包括除尘，擦拭，擦洗，清扫或拖地，使他们能够使用人类工具。 清洁是日常生活的一项重要活动，也是维持生活质量的一项重要任务。许多老年人失去了独立性，当他们不再能够进行清洁任务。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等专用机器人。清洁度对于酒店或百货商店等许多商业设施至关重要，这些设施涉及大型表面，需要以本项目开发的可编程清洁机器人可以处理的特定方式定期清洁。通用清洁机器人需要能够使用许多不同的清洁工具，在许多不同的环境中，根据许多不同的用户偏好。可能情况的多样性使得难以开发用于任意清洁任务的通用控制器或规划器。此外，不可能提前知道用户的偏好。为了应对这些挑战，该项目开发了一种从演示中学习（LfD）技术，允许机器人从人类演示中学习清洁动作。这种技术利用了清洁任务的共同结构：它们都涉及相对于具有特定图案的目标表面移动清洁工具，同时在表面上施加一定的力。虽然已经存在许多LfD方法，但它们都不允许学习推广到具有任意清洁模式的任意表面的清洁动作。该项目提供了新的表示和学习算法，使移动的机械手能够使用不同类型的清洁工具清洁任意大小、形状、曲率、污垢分布和杂乱的表面。在该项目中开发的方法进行评估与真实的清洁任务，通过系统的实验和用户研究在实验室中，以及在真实的世界中，通过部署在一个私人家庭中的相同的机器人。

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.