Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
Grounded Symbolic Communication between Heterogeneous Cooperating Robots
Autonomous Robots
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Transferring embodied concepts between perceptually heterogeneous robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
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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 show that 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 properties derived from different modalities: color, texture, shape, and size. Using our methods, two heterogeneous robots utilizing different sensors and representations are able to successfully transfer support vector machine (SVM) classifiers among each other, resulting in speedups during learning.