Continuous Rapid Learning by Human Imitation using Audio Prompts and One-Shot Learning

In the general field of collaborative robotics, one of the topics of greatest interest to the scientific community is the ability to learn to perform certain actions by imitating humans. If we think about humans, when someone teaches us how to perform a certain action, we often need to be shown just one time how to do it. Likewise, we believe that robotics should follow this line, using models that do not involve the capture of huge data sets or exhaustive training. Furthermore, while general models can typically be pretrained offline, the robot must quickly adapt to new knowledge without requiring an expensive retraining process. In this article we present a flexible neural learning architecture that allows a robot to learn how-to pick-up a given object just by watching how a human does it. Then, the robot will be able to pick up the current object, or other objects previously learned, anywhere in the work field, with a simple audible indication from the user. This is achieved based on continuous incremental learning techniques and generic segmentation networks integrated with Siamese network models according to the recently proposed CP-CVV method. Results are presented for the success rate in grasping a varied set of objects.