Transferring Embodied Concepts Between Perceptually Heterogeneous Robots

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Title: Transferring Embodied Concepts Between Perceptually Heterogeneous Robots
Author: Kira, Zsolt
Abstract: This paper explores methods and representations that allow two perceptually heterogeneous robots, each of which represents concepts via grounded properties, to transfer knowledge despite their differences. This is an important issue, as it will be increasingly important for robots to communicate and effectively share knowledge to speed up learning as they become more ubiquitous.We use Gӓrdenfors’ conceptual spaces to represent objects as a fuzzy combination of properties such as color and texture, where properties themselves are represented as Gaussian Mixture Models in a metric space. We then use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot. These mappings are then used to transfer a concept from one robot to another, where the receiving robot was not previously trained on instances of the objects. We show in a 3D simulation environment that these models can be successfully learned and concepts can be transferred between a ground robot and an aerial quadrotor robot.
Type: Paper
Date: 2009
Contributor: Georgia Institute of Technology. College of Computing
Georgia Institute of Technology. Mobile Robot Laboratory
Publisher: Georgia Institute of Technology
Subject: Heterogeneous robots
Modeling robots
Object-recognition domain

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