Transferring Embodied Concepts Between Perceptually Heterogeneous Robots

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

A Conceptual Space Architecture for Widely Heterogeneous Robotic Systems

This paper describes the value of the conceptual space approach for use in teams of robots that have radically different sensory capabilities. The formal underpinnings and perceptual processes are described in the context of a biohazard detection task. The architecture is based on the conceptual spaces representation that Gärdenfors suggested as an alternative to more traditional AI approaches