One-Shot Imitation Learning on Heterogeneous Associated Tasks via Conjugate Task Graph

Abstract

One-shot imitation learning is one of the crucial topics in robot learning with the pursuit of higher intelligence. Recently, conjugate task graph (CTG) network has been applied to generalize the imitation of homogeneous tasks based on a single video demonstration, where a standard optimization method is utilized to update the parameters of graph neural network. Nevertheless, when dealing with heterogeneous associated tasks, the standard algorithm needs to be improved to acquire higher learning accuracy. Given a set of heterogeneous tasks containing N sets of homogeneous tasks, we propose an N -Step Alternating Optimization in CTG (NSAO-CTG) to accomplish a superior learning, where each step incorporates the nodes and edges corresponding to a new set of homogeneous tasks. Furthermore, NSAO-CTG with a novel update rule for the node localizer and edge classifier (NSAO-CTG+) is proposed for execution based on the association information between tasks. Extensive experiments demonstrate the effectiveness of the proposed method in one-shot imitation learning of heterogeneous associated tasks.