Transfer of sparse coding representations and object classifiers across heterogeneous robots

This paper examines the problem of transfer learning in the context of object recognition in a heterogeneous robot team. We specifically look at the case where robots individually learn object classifiers and must then transfer the resulting learned knowledge to another robot. Recent trends in computer vision and robotics have moved towards feature representation learning, where the underlying feature representation used in classification is learned in a data-driven way. This poses a problem to knowledge transfer, as the underlying representations learned by different robots will differ significantly. In this paper, we present several hypotheses with regard to knowledge transfer in such a scenario, specifically that 1) the transfer of knowledge will be most effective if it involves not just the classifier itself, but the learned feature representations themselves, 2) this is not a problem because given similar scenes and objects, some methods such as sparse coding are able to learn representations that can be successfully used by another robot, and 3) a codebook encoding scheme such as Fisher vectors will result in a smaller reduction in accuracy after transfer even if the receiving robot uses its own learned feature representation. Finally, we contribute an alignment procedure and demonstrate that it can serve to facilitate knowledge transfer even when the underlying feature representations are independently learned by each robot and codebook methods are not used. We test all three of the hypotheses and the alignment procedure on a real-world dataset consisting of two robots viewing the same 12 objects using cameras with differing characteristics.