Learning generalizable surface cleaning actions from demonstration

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.