Coordination of multiple robots using graph neural networks

A method performed by one or more computers and for controlling a plurality of robots to cause the robots to perform a task that involves moving to a plurality of target locations in an environment, the method comprising repeatedly performing the following operations:

• performing a plurality of planning iterations starting from a current state of the environment to generate plan data, wherein performing each planning iteration comprises:
• traversing through states of the environment starting from the current state until a leaf state of the environment is reached;
• generating, from state data characterizing the leaf state, graph data representing a graph of the leaf state of the environment, the graph comprising a plurality of nodes and a plurality of edges, wherein each edge connects a respective pair of nodes from the plurality of nodes, wherein the plurality of nodes comprises a respective robot node for each of the robots and a respective target node for each of the target locations, and wherein the graph data comprises a respective initial feature representation for each of the robot nodes and for each of the target nodes;
• processing the graph data using a graph neural network to generate a graph output that comprises a respective updated feature representation for each of the robot nodes;
• generating, from the graph output, an update to the plan data;
• and
• updating the plan data using the generated update;
• and
• after performing the plan data, selecting an action using the plan data.

A system comprising:

• one or more computers;
• and
• one or more storage devices storing instructions that when executed by the one or more computers, cause the one or more computers to perform the operations comprising:
• obtaining state data representing a current state of the environment;
• generating, from the state data, graph data representing a graph of the current state of the environment, the graph comprising a plurality of nodes and a plurality of edges, wherein each edge connects a respective pair of nodes from the plurality of nodes, wherein the plurality of nodes comprises a respective robot node for each of the robots and a respective target node for each of the target locations, and wherein the graph data comprises a respective initial feature representation for each of the robot nodes and for each of the target nodes;
• processing the graph data using a graph neural network to generate a graph output that comprises a respective updated feature representation for each of the robot nodes;
• and
• selecting, based on the graph output, a respective action to be performed by each of the robots.