Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients

• URL: http://arxiv.org/abs/2102.06284v1
• Date: Thu, 11 Feb 2021 21:57:43 GMT
• Title: Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients
• Authors: Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro
• Abstract summary: 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. We employ a graph neural network (GNN) to parameterize policies for the robots.
• Score: 122.85280150421175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, 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.

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