One-Shot Imitation Learning With Graph Neural Networks for Pick-and-Place Manipulation Tasks

The proposed work presents a framework based on Graph Neural Networks (GNN) that abstracts the task to be executed and directly allows the robot to learn task-specific rules from synthetic demonstrations given through imitation learning. A graph representation of the state space is considered to encode the task-relevant entities as nodes for a Pick-and-Place task declined at different levels of difficulty. During training, the GNN-based policy learns the underlying rules of the manipulation task focusing on the structural relevance and the type of objects and goals, relying on an external primitive to move the robot to accomplish the task. The GNN-policy has been trained as a node-classification approach by looking at the different configurations of the objects and goals present in the scene, learning the association between them with respect to their type for the Pick-and-Place task. The experimental results show a high generalization capability of the proposed model in terms of the number, positions, height distributions, and even configurations of the objects/goals. Thanks to the generalization, only a single image of the desired goal configuration is required at inference time.