An Architecture for Tool Use and Learning in Robots

In this paper we address the problem of a robot learning to use environmental objects as tools, in order to help it achieve its goals. Learning to use an object as a tool involves understanding which goals it helps an agent to achieve, the properties of the tool that make it useful, and how the tool must be manipulated in the environment in order to achieve the desired goal. A cup, for example, can be use to hold objects or liquids, should be of the appropriate size and shape (concave-up), and needs to be held the right way up. We present an architecture for a robot agent that is able to learn about objects in this way, and thereby employ appropriate objects as tools to help it achieve its goals. Our agent learns through demonstration and experiment, with the main generalisation module being an Inductive Logic Programming algorithm.