Transfer Learning of Visual Concepts across Robots: a Discriminative Approach

While there is a general consensus that autonomous robots should be able to learn continuously over time, the learning process is traditionally envisioned for each specific robot situated in a given environment. This does not consider the fact that robots performing similar tasks in similar settings would probably learn similar concepts. They would therefore benefit by sharing their prior experience with each other. In this paper we present a transfer learning algorithm that enables robots located in different places to take advantage of each other’s experience, boosting the learning process. We specifically assume to have robots equipped with a camera. We do not make any assumption on the type of camera, nor on where it is positioned. We also assume that the robots use the same feature descriptors and learning algorithms. Under these assumptions, we show that one robot can hugely benefit from what has been learned by peer robots performing similar tasks. The advantage concretely means a consistent boost in performance, especially when training data is scarce. Our algorithm is based on Least Square Support Vector Machine, and allows to determine automatically from where to transfer and how much to transfer: this makes it possible to take advantage of the prior when it is useful, while minimizing the risk of negative transfer when the priors are not informative. Thorough experiments on four different publicly available databases show the power of our approach.