Learning Generalizable Surface Cleaning Actions from Demonstration
通过演示学习通用的表面清洁措施
Abstract— When surveyed, potential users often report cleaning as a desired robot capability. Cleaning tasks, such as dusting, wiping, or scrubbing, involve applying a tool on a surface. A general-purpose robotic solution to household cleaning needs to address manipulation of the numerous cleaning tools made for different purposes. Finding a universal solution to this manipulation problem is extremely challenging and it is not feasible for developers to pre-program the robot to use every possible tool. Instead, our work seeks to allow end users to program robots by demonstration using their own specific tools. We propose a method to extract a compact representation of a cleaning action from a single demonstration, such that the tool can be applied on different surfaces. The method exploits key insights about tool directionality and constraints placed on the provided demonstration. We demonstrate that our method is able to reliably learn cleaning actions for six different tools and apply those actions on different testing surfaces, even ones smaller than the training surface. Our method reproduces the cleaning performance of the demonstrated trajectory when applied on the training surface and it captures different user preferences.
进行调查时，潜在用户经常将清洁工作报告为所需的机器人功能。清洁任务（例如除尘，擦拭或擦洗）涉及在表面上使用工具。用于家庭清洁的通用机器人解决方案需要解决为不同目的而制造的众多清洁工具的操纵。寻找一个通用的解决方案来解决这个操纵问题极具挑战性，并且开发人员无法对机器人进行预编程以使用所有可能的工具是不可行的。相反，我们的工作旨在允许最终用户通过演示使用自己的特定工具对机器人进行编程。我们提出了一种方法，可以从一次演示中提取清洁动作的紧凑表示，以便可以将工具应用于不同的表面。该方法利用了有关工具方向性和约束条件的关键见解，这些约束条件和所提供的演示都存在约束。我们证明了我们的方法能够可靠地学习六种不同工具的清洁动作，并将这些动作应用到不同的测试表面上，甚至小于训练表面。当应用到训练表面上时，我们的方法再现了演示轨迹的清洁性能，并且捕获了用户的不同偏好。