Mapping Grounded Object Properties across Perceptually Heterogeneous Embodiments

As robots become more common, it becomes increasingly useful for them to communicate and effectively share knowledge that they have learned through their individual experiences. Learning from experiences, however, is oftentimes embodiment-specific; that is, the knowledge learned is grounded in the robot's unique sensors and actuators. This type of learning raises questions as to how communication and knowledge exchange via social interaction can occur, as properties of the world can be grounded differently in different robots. This is especially true when the robots are heterogeneous, with different sensors and perceptual features used to define the properties. In this paper, we present methods and representations that allow heterogeneous robots to learn grounded property representations, such as that of color categories, and then build models of their similarities and differences in order to map their respective representations. We use a conceptual space representation, where ob...

• Type: Journal
• Year: 2009
• Where: FLAIRS
• Authors: Zsolt Kira
• Keywords: Artificial Intelligence, FLAIRS 2009, Gaussian Mixture Models, Heterogeneous Pioneer Robots, Robots