Tool Use Learning for a Real Robot - Handy Wicaksono; Claude Sammut (International Journal of Electrical and Computer Engineering (IJECE), 2018)
A robot may need to use a tool to solve a complex problem. Currently, tool use must be pre-programmed by a human. However, this is a difficult task and can be helped if the robot is able to learn how to use a tool by itself. Most of the work in tool use learning by a robot is done using a feature-based representation. Despite many successful results, this representation is limited in the types of tools and tasks that can be handled. Furthermore, the complex relationship between a tool and other world objects cannot be captured easily. Relational learning methods have been proposed to overcome these weaknesses [1, 2]. However, they have only been evaluated in a sensor-less simulation to avoid the complexities and uncertainties of the real world. We present a real world implementation of a relational tool use learning system for a robot. In our experiment, a robot requires around ten examples to learn to use a hook-like tool to pull a cube from a narrow tube.