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Claude Sammut
University of New South Wales
Computer Science & IT Electrical & Electronics Engineering

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Tool Use Learning for a Real Robot Handy Wicaksono; Claude Sammut
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 2: April 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2126.723 KB) | DOI: 10.11591/ijece.v8i2.pp1230-1237

Abstract

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.
A cognitive robot equipped with autonomous tool innovation expertise Handy Wicaksono; Claude Sammut
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (658.154 KB) | DOI: 10.11591/ijece.v10i2.pp2200-2207

Abstract

Like a human, a robot may benefit from being able to use a tool to solve a complex task. When an appropriate tool is not available, a very useful ability for a robot is to create a novel one based on its experience. With the advent of inexpensive 3D printing, it is now possible to give robots such an ability, at least to create simple tools. We proposed a method for learning how to use an object as a tool and, if needed, to design and construct a new tool. The robot began by learning an action model of tool use for a PDDL planner by observing a trainer. It then refined the model by learning by trial and error. Tool creation consisted of generalising an existing tool model and generating a novel tool by instantiating the general model. Further learning by experimentation was performed. Reducing the search space of potentially useful tools could be achieved by providing a tool ontology. We then used a constraint solver to obtain numerical parameters from abstract descriptions and use them for a ready-to-print design. We evaluated our system using a simulated and a real Baxter robot in two cases: hook and wedge. We found that our system performs tool creation successfully.
Co-Authors Handy Wicaksono Handy Wicaksono