Inter-robot transfer learning for perceptual classification

We introduce the novel problem of inter-robot transfer learning for perceptual classification of objects, where multiple heterogeneous robots communicate and transfer learned object models consisting of a fusion of multiple object properties. Unlike traditional transfer learning, there can be severe differences in the data distributions, resulting from differences in sensing, sensory processing, or even representations, that each robot uses to learn. Furthermore, only some properties may overlap between the two robots. We t in such cases, the abstraction of raw sensory data into an intermediate representation can be used not only to aid learning, but also the transfer of knowledge. Further, we utilize statistical metrics, learned during an interactive process where the robots jointly explore the environment, to determine which underlying properties are shared between the robots. We demonstrate results in a visual classification task where objects are represented via a combination of p...

• Type: Conference
• Year: 2010
• Where: ATAL
• Authors: Zsolt Kira
• Keywords: ATAL 2010, Heterogeneous Robots, Intelligent Agents, Inter-robot Transfer, Robots