Toward Robot Learning of Tool Manipulation from Human Demonstration

Robots that manipulate everyday tools in unstructured, human settings could more easily work with people and perform tasks that are important to people. Task demonstration could serve as an intuitive way for people to program robots to perform tasks. By focusing on task-relevant features during both the demonstration and the execution of a task, a robot could more robustly emulate the important characteristics of the task and generalize what it has learned. In this paper we describe a method for robot task learning that makes use of the perception and control of the tip of a tool. For this approach, the robot monitors the tool’s tip during human use, extracts the trajectory of this task relevant feature, and then manipulates the tool by controlling this feature. We present preliminary results where a humanoid robot learns to clean a flexible hose with a brush. This task is accomplished in an unstructured environment without prior models of the objects or task.