Résumé
This study proposes a visuo-tactile Zero-Shot object recognition framework. The proposed framework recognizes a set of novel objects for which no tactile or visual training data are available. It uses visuo-tactile training data collected from known objects to recognize the novel ones, given their attributes. This framework extends the haptic Zero-Shot Learning framework that we proposed in [1] with vision, which enables a multimodal recognition system. In our test with the PHAC-2 dataset, the system was able to get a recognition accuracy of 72% among 6 objects that were never touched or seen during the training phase.