In the wake of increasing interests in artificial intelligence, problems such as massive training data requirementssingle task learning constraint are unavoidable. Thus we propose a Learning from Demonstration (LfD) system. The LfD uses deep learning techniques including image recognition, actionfacial recognition, etc. As such, robots learn actions directly from human instead of trainin

To allow robots learn from human demonstration, action recognitionobject detection are integrated in the LfD. An action is recognizedfragmented into sub-actions using I3D deep learning architecture. YOLOv3 is used to detectlocalize the objects in the environment. An action base is therefore developed based on action recognitionobject detection, such that the robot can learn dem

The proposed LfD can be employed in indoor environments, especially in circumstances like home services. Because of the capabilities that enable robots to learn from repeatedly human demonstrations, LfD can be employed in various different environments. Also, LfD combines actions with objects such that robots are able to learn the purposes of actions rather than only their appearances.