Large Scale Distributed Collaborative Unlabeled Motion Planning With Graph Policy Gradients

• Published in: IEEE robotics and automation letters Vol. 6; no. 3; pp. 5340 - 5347
• Main Authors: Khan, Arbaaz, Kumar, Vijay, Ribeiro, Alejandro
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Learning; Motion planning; Multiagent systems; Neural networks; Planning; Policies; Robot kinematics; Robot sensing systems; Robots; Task analysis; Training; Transmission line matrix methods
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3074885

In this letter, we present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. To solve the problem of arbitrary dynamics and constraints we propose formulating the problem as a multi-agent problem. We are able to demonstrate the scalability of our methods for a large number of robots by employing a graph neural network (GNN) to parameterize policies for the robots. The GNN reduces the dimensionality of the problem by learning filters that aggregate information among robots locally, similar to how a convolutional neural network is able to learn local features in an image. Additionally, by employing a GNN we are also able to overcome the computational overhead of training policies for a large number of robots by first training graph filters for a small number of robots followed by zero-shot policy transfer to a larger number of robots. We demonstrate the effectiveness of our framework through various simulations.