Towards One Shot Learning by Imitation for Humanoid Robots

Teaching a robot to learn new knowledge is a repetitive and tedious process. In order to accelerate the process, we propose a novel template-based approach for robot arm movement imitation. This algorithm selects a previously observed path demonstrated by a human and generates a path in a novel situation based on pairwise mapping of invariant feature locations present in both the demonstrated and the new scenes using a combination of minimum distortion and minimum energy strategies. This One-Shot Learning algorithm is capable of not only mapping simple point-to-point paths but also adapting to more complex tasks such as those involving forced waypoints. As compared to traditional methodologies, our work require neither extensive training for generalisation nor expensive run-time computation for accuracy. This algorithm has been statistically validated using cross-validation of grasping experiments as well as tested for practical implementation on the iCub humanoid robot for playing the tic-tac-toe game.